myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR

Htet Arkar; Min Thiha Tun; Myat Oo Swe; Thazin Myint Oo; Thepchai Supnithi; Thet Htet San; Thura Aung; Ye Bhone Lin; Ye Kyaw Thu

REVIEW 4 major objections 6 minor 300 references

myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A mid-size Whisper fine-tuned on 28 hours of Burmese clinical speech hits 23.44% word error rate, beating far larger general-purpose models.

desk verdict Useful new Burmese medical speech resource, but the state-of-the-art claim is not supported by the evidence and the tables need cleaning. read the letter →

arxiv 2608.11036 v1 pith:F7A246CG submitted 2026-08-11 cs.CL

Ye Kyaw Thu , Ye Bhone Lin , Thura Aung , Htet Arkar , Myat Oo Swe , Thet Htet San , Min Thiha Tun , Thazin Myint Oo

show 1 more author

Thepchai Supnithi

This is my paper · ORCID

classification cs.CL

keywords BurmeseASRmedicalspeechrecognitionWhisperfine-tuninglow-resourcedataaugmentationsimulatedroomacousticsparameter-efficientclinicaldialogue

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a mid-size Whisper model fully fine-tuned on a 28-hour, native-verified Burmese medical speech corpus reaches a word error rate of 23.44% on clean clinical dialogue, beating much larger general-domain fine-tuned models. The authors argue that domain-specific adaptation is more consequential than raw model scale for specialized medical vocabulary and clinical speaking style, and that the released corpus makes this reproducible. They also show that waveform- and spectrogram-level data augmentation improves robustness to noise and room reverberation at a small cost to clean-speech accuracy, and that parameter-efficient fine-tuning with rank-stabilized LoRA can adapt Whisper-Large-v2 under tight memory constraints. If correct, this provides a concrete recipe for building clinical ASR for low-resource tonal languages.

What carries the argument

The central object is the newly constructed mediTalk-mm-rdy corpus: 14,517 verified Burmese sentences from the Samson PLAB 2 handbook, read by nine native speakers (two male, seven female) and manually checked at syllable level for acoustic fidelity, yielding 28 hours 6 minutes 36 seconds of speech. From this, 52.95 hours are used for training and 2.87 hours for evaluation, and simulated room impulse responses (L-shaped, short/long distance) generated by Pyroomacoustics create reverberant training samples. The fine-tuning pipeline compares full fine-tuning (FFT) with parameter-efficient rank-stabilized LoRA (rsLoRA, $r=128$, $\alpha=256$, dropout 0.05 applied to the query and value projections) and expands training data 3.75$\times$ via a 50% sample, three waveform transforms (time shifting, pitch shifting, additive Gaussian noise) and two SpecAugment masks (time and frequency masking). The comparison of WER across Whisper Tiny through Large-v2, with and without augmentation and under controlled SNR and room types, carries the argument that domain-specific fine-tuning on high-quality validated data, not model size alone, drives accuracy.

What would settle it

Record a separate set of Burmese clinical dialogues in an actual clinic (with real device noise, multi-speaker babble, and microphone characteristics), run myMediWhisper-Medium both with and without augmentation alongside whisper-large-v3-myanmar, and check whether the augmented model beats the unaugmented one and whether myMediWhisper-Medium keeps its WER advantage; if either fails, the paper's robustness-transfer and domain-superiority claims would be contradicted.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that a mid-size Whisper model, fully fine-tuned on a small but carefully validated Burmese medical speech corpus, reaches a state-of-the-art word error rate of 23.44% on a held-out clean test set of clinical dialogues, outperforming general-domain fine-tuned models that are several times larger (whisper-large-v3-myanmar at 32.03% and MMS-1B at 37.90%). This best result is obtained without data augmentation; applying time/pitch shifts, additive Gaussian noise, and SpecAugment masks slightly degrades clean-speech WER (Medium Aug reaches 24.73%) but substantially improves robustness under low signal-to-noise ratios and in simulated reverberant rooms. Under memory constraints, parameter-efficient fine-tuning with rank-stabilized LoRA ($r=128$) adapts Whisper-Large-v2 to 41.57% WER, enabling large-model adaptation on limited hardware at the cost of higher inference latency. Syllable-level error analysis attributes residual errors primarily to voiced/unvoiced plosive confusions and the deletion of weak nominal prefixes and grammatical particles.

Load-bearing premise

The robustness conclusions rely on simulated room impulse responses and additive Gaussian noise behaving like real clinical acoustics, so the measured robustness gains are assumed to transfer to actual hospital environments; the authors acknowledge this limitation in Section 7.

Editorial extensions

If this is right

The publicly released corpus enables reproducible benchmarking of Burmese medical ASR and downstream clinical speech tasks such as speech translation and medical named entity recognition.
Full fine-tuning of a mid-size Whisper model on a small domain corpus is a practical recipe that outperforms far larger general-domain models, indicating that domain match and data quality can outweigh model scale for specialized dictation.
Parameter-efficient fine-tuning with rsLoRA at $r=128$ unlocks Whisper-Large-v2 adaptation under memory limits, broadening the hardware on which low-resource languages can be served, though inference becomes slower.
The augmentation trade-off yields deployment guidance: use the unaugmented model for controlled environments and the augmented model for noisy or reverberant clinics.
The syllable-level error analysis identifies specific phonetic and orthographic failure modes (voicing contrasts, deletion of weak prefixes/particles) that future work can target with tonal modeling or post-processing.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the pattern holds, other low-resource tonal languages with a small, natively verified corpus of specialist speech may see similar gains from domain-specific Whisper fine-tuning at mid-size scale, even without large general-domain data.
The clean-versus-robust trade-off suggests a deployment strategy the authors do not explicitly propose: training two copies of the model (one clean, one augmented) and selecting by estimated noise level at runtime.
Because the robustness conclusions rest on simulated acoustics, the imminent plan to collect real clinical recordings will directly test whether the augmentation advantage transfers; until then, the clean-speech WER claim is more firmly evidenced than the robustness claim.
The error analysis hints that explicitly modeling Burmese tonal and glottal features (such as the Asat marker) could further reduce WER, but this design hypothesis is not tested in the paper.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The real contribution here is the corpus: 28 hours of Burmese medical speech, nine native speakers, syllable-level validation, and a public release on Hugging Face if the link stays live. That is a genuinely reusable resource for a low-resource domain, and the per-speaker stats in Table 1 are useful. The fine-tuning comparison (FFT vs rsLoRA PEFT) and the robustness analysis are standard but competently executed, and the syllable-level error analysis with IPA confusion pairs is a nice touch. Give credit where it is due: the empirical work is honest in its setup, and the paper gives enough detail to reproduce the pipeline.

Now the soft spots, in descending order of seriousness. First, the headline: “state-of-the-art WER of 23.44%” is not established. The paper itself cites myMediCon at 19.0% in Section 1 but never benchmarks against it. You cannot claim SOTA while ignoring a better existing number. Second, the test set is not speaker-independent. Every one of the nine speakers appears in both the Train and Test columns of Table 1, and the paper never mentions a speaker-disjoint split. The external baselines in Table 2 are zero-shot, so they have not seen these voices, while the FFT and PEFT models have trained on other utterances from the same speakers. That gives the fine-tuned models an inherent advantage that is not about domain adaptation alone. The SOTA claim should be withdrawn or heavily qualified until a speaker-independent evaluation is reported.

Third, the tables are sloppy. Several Table 2 rows do not decompose correctly (e.g., PEFT Tiny: 66.00 + 22.95 + 42.96 = 131.91, not 115.68). Table 1 totals do not match row sums: the train utterance column sums to 26,264, not 20,264, and duration sums are off by about an hour. The RTF paragraph contradicts itself: it says PEFT increases RTF but then lists a decrease from 0.691 to 0.408 for Whisper-Medium. These are fixable copy-editing problems, but they are numerous enough to undermine confidence in the reported numbers until corrected.

Finally, the robustness conclusions rest on simulated noise and room impulse responses. The authors acknowledge this as a limitation, and the stress test worried about it. That is a fair limitation, not a fatal flaw — simulated acoustics are common in ASR robustness work. Just do not over-read the results as proven clinical robustness.

Is a serious referee warranted? Yes, because the corpus is valuable and the comparison framework is worth publishing after a major revision. The paper needs a corrected evaluation protocol, a disavowal of the unsupported SOTA claim, and a thorough pass on the tables and text. If those get fixed, this is a solid empirical contribution for the low-resource ASR community.

Referee Report

4 major / 6 minor

Summary. The paper introduces myMediWhisper, a new 28-hour Burmese medical speech corpus recorded and validated by native speakers, and uses it to fine-tune Whisper models with full fine-tuning (FFT) and parameter-efficient fine-tuning (PEFT) via rsLoRA. The authors evaluate ASR performance on clean speech and under simulated noise and room acoustics, report that augmentation improves robustness at the cost of clean-speech accuracy, and claim a state-of-the-art WER of 23.44% for the fully fine-tuned myMediWhisper-Medium without augmentation, outperforming larger general-domain fine-tuned models. They also provide a syllable-level error analysis showing voicing/tonal confusions and deletions of weak syllables and grammatical particles.

Significance. If the central claims hold, the paper makes two valuable contributions: it releases a publicly available, native-validated Burmese medical speech corpus, which is rare for this language and domain, and it demonstrates that a mid-size Whisper model can be adapted to a specialized medical domain with limited data. The systematic comparison of FFT and PEFT, the controlled robustness analysis, and the error analysis are useful for practitioners. However, the headline 'state-of-the-art' claim is not currently supported because the evaluation set overlaps in speakers with the training set, and the paper does not benchmark against the 19.0% myMediCon WER it cites as the best prior baseline. The empirical comparison trends are plausible, but the internal inconsistencies in Tables 1 and 2 need to be resolved before the quantitative results can be trusted.

major comments (4)

[§5.1, Table 1] The state-of-the-art claim is not supported by a speaker-independent evaluation. Table 1 lists every speaker in both the Train and Test columns (e.g., sp01: 7,364 train / 410 test; sp09: 900 train / 50 test), and the paper never describes a speaker-disjoint split. Because the fine-tuned models have seen other utterances from the same speakers while the external baselines in Table 2 are zero-shot on this corpus, the comparison conflates domain adaptation with speaker adaptation. The 23.44% WER should also be compared with the 19.0% myMediCon baseline cited in Section 1 before 'state-of-the-art' is used.
[Table 2] The error decomposition is internally inconsistent: for several rows SER + DER + IER does not equal the reported WER. For example, PEFT Tiny lists 66.00 + 22.95 + 42.96 = 131.91 but reports WER = 115.68; PEFT Base lists 76.03 + 10.35 + 17.49 = 103.87 but reports 99.57; PEFT Tiny Aug lists 45.90 + 44.76 + 11.79 = 102.45 but reports 92.28. These mismatches indicate a data-processing or reporting error that affects the reliability of all comparisons in this table.
[Table 1] The totals in Table 1 do not match the sum of the row values. The train durations sum to 51.95 hours, not 52.95; the test durations sum to 2.89 hours, not 2.87; and the total durations sum to 54.84 hours, not 55.82. In addition, the text states that the verified corpus contains 28 hours 6 minutes 36 seconds, and the table reports 55.82 total hours 'after adding simulated speech with different room acoustics' (Table 1 caption); the relationship between the original corpus size and the reported training/test durations is not explained.
[§5.2, Figures 2-3, §7 Limitations] The claim that models 'generalize reasonably well to unseen room acoustics and noisy conditions' (Section 5.2) is overstated because the robustness test uses the same class of simulated room impulse responses and additive Gaussian noise that were used in the training augmentation. The test conditions are not acoustically independent of the training conditions, and the paper's own Limitations section acknowledges that real-world clinical acoustics may differ. The qualitative conclusion should be framed as robustness to matched simulated variation rather than generalization to unseen acoustic environments.

minor comments (6)

[Abstract, §5.1, §7] The term 'state-of-the-art' is used in the Abstract and Conclusion without comparison to the 19.0% myMediCon baseline or a speaker-independent evaluation; it should be replaced by a qualified claim such as 'best on our evaluation set'.
[§5.1] The sentence beginning 'While the open-source fine-tuned whisper-large-v3-myanmar (chuuhtetnaing, 2024) achieves a baseline WER of 32.03% and sil-ai/wav2vec2-bloom-speech-mya (SIL Global - AI, 2022) , our FFT...' is grammatically incomplete; it appears to be missing a verb or comparison for the second model.
[Table 2] The chrF values of 1e-16 for the zero-shot Whisper models are suspiciously near zero and may be a formatting or computation artifact; please report actual chrF scores or explain why they are effectively zero.
[§4, Table 1] The paper reports a verified corpus of 28 hours 6 minutes 36 seconds but later says 52.95 hours of speech were allocated for training; please clarify how the simulated room acoustics expand the data and how the numbers in Figure 1 and Table 1 relate to the original corpus.
[Figure 3] The caption does not state which fine-tuning strategy (FFT or PEFT) is used for 'Whisper Medium' and 'Whisper Medium with augmentation'; please specify the configuration to avoid ambiguity.
[§3.2, Eq. (3)] The scaling factor in Equation (3) is written as α√r, while the text describes 'a modified scaling factor α/√r'; the notation should be made consistent (e.g., α/√r) and the forward equation should use parentheses to avoid ambiguity.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity; the empirical fine-tuning results are self-contained, though the SOTA label is over-stated relative to the cited myMediCon baseline.

full rationale

The paper is an empirical ASR study; there is no mathematical derivation chain in which an output is defined from an input. Whisper and rsLoRA equations (Eq. 1-3) are standard model definitions, not derived predictions. The central result, the 23.44% WER, is measured on a held-out 2.87-hour test set and compared against external zero-shot baselines; it is not a fitted parameter disguised as a prediction. The paper cites two prior works with overlapping authors: Ei San et al. (2022) as the transcript source and Htun et al. (2024, myMediCon) as background and as the 'best baseline WER of 19.0%'. Neither citation is load-bearing for the fine-tuning results: the transcript source is a data resource, and the myMediCon baseline is not used to compute any reported number and is in fact not benchmarked against, giving rise to a correctness concern (the unqualified 'state-of-the-art' label in the Abstract/Conclusion is not supported by the cited 19.0% WER, and Table 1 shows all speakers appear in both train and test, so the comparison with zero-shot models does not establish speaker-independent SOTA). These are validity/overclaim issues, not circularity. Section 7's limitation that simulated noise/RIRs may not capture real clinical dynamics is an external-validity caveat, not a circular step, because the robustness evaluation is generated post-hoc by the same simulator used in training; this is distribution overlap, not a definitional equivalence. No self-citation chain forces the conclusions.

Assumptions & free parameters 6 free parameters · 3 assumptions · 0 invented entities

The central claim depends on standard ASR hyperparameters and simulation choices, but no constants are fitted to the test set. The free parameters are hand-chosen configuration values, not fitted to data.

free parameters (6)

LoRA rank r = 128
Chosen by hand for the PEFT experiments; affects the parameter count and latency claims.
LoRA scaling alpha = 256
Chosen by hand, paired with r=128 in rsLoRA.
Learning rate = 1e-5
Single-epoch training with fixed LR; standard choice, not tuned on a validation split.
Augmentation sampling ratio = 0.5 (50% of train data)
Randomly sampled subset; chosen to produce a 3.75x data increase.
SpecAugment masking probabilities = time mask p=0.3, freq mask p=0.1
Time mask width 10 steps, frequency mask width 64 bands; standard values from SpecAugment.
Room simulation parameters = not fully specified (L-shaped, random-distance room types)
The paper does not give absorption, reverberation time, or room dimensions, so replication depends on Pyroomacoustics defaults.

assumptions (3)

domain assumption The PLAB 2 handbook translations, verified by two native speakers, are medically accurate and representative of clinical dialogue.
The corpus and all conclusions rely on this transcript source being a valid proxy for real clinical conversation (Section 4).
domain assumption Simulated room acoustics and Gaussian noise capture the acoustic variability of real clinical environments.
The robustness evaluation in Section 5.2 uses these simulations, and the authors list this as a limitation in Section 7.
standard math Whisper, LoRA/rsLoRA, and SpecAugment behave as described in their original papers.
The experimental setup relies on these external implementations (Sections 3.1 to 3.2).

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Cite this review

Pith. "Pith review of myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR." pith.science (2026). https://pith.science/paper/F7A246CG

@misc{pith2026260811036,
  author       = {Pith},
  title        = {Pith review of: myMediWhisper: Construction of Burmese Medical Speech Corpus and Whisper Fine-Tuning for Clinical Dialogue ASR},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F7A246CG}},
  note         = {Machine review of arXiv:2608.11036}
}

read the original abstract

Although Whisper models benefit from large-scale multilingual pre-training, their performance on Burmese medical speech remains limited. This work presents a Burmese medical speech recognition framework built on a high-quality 28-hour corpus recorded and validated by native speakers. We fine-tune Whisper models using full fine-tuning (FFT) and parameter-efficient fine-tuning (PEFT) with LoRA. To evaluate robustness, we apply waveform- and spectrogram-level data augmentation under controlled noise and simulated room acoustics. While augmentation reduces performance on clean speech, it significantly improves robustness in noisy and reverberant environments across FFT and PEFT settings. Our best-performing system, fully fine-tuned myMediWhisper-Medium without augmentation, achieves a state-of-the-art Word Error Rate (WER) of 23.44%, outperforming much larger general-domain fine-tuned models. Dataset and other resources can be found at the Huggingface repository: https://huggingface.co/datasets/LULab/mediTalk-mm-rdy.

Figures

Figures reproduced from arXiv: 2608.11036 by the authors.

**Figure 2.** WER under varying Signal-to-Noise Ratio (SNR) conditions for myMediWhisper models trained with and without data augmentation. Male Female Short Distance Long Distance L-Shape 0 5 10 15 20 25 30 15.98 24.43 14.32 24.94 16.18 28.46 Male Female Short Distance Long Distance L-Shape 0 5 10 15 20 25 30 17.63 21.41 18.05 22.17 18.88 25.69 ROOM TYPES ROOM TYPES W o r d E r r o r R a t e ( W E R ) % Whisper Medium Whisper Me… view at source ↗

**Figure 3.** Effect of simulated room acoustics on WER [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Syllable-level error breakdown for the myMediWhisper Medium Aug model. Phonological and Orthographic Substitutions. Substitutions formed a primary source of alignment errors, driven by acoustic similarity, tone shifts, and colloquial orthographic variants. The most fre…

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A Sequence Modelling Approach to Question Answering in Text-Based Games

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Automatic Exploration of Textual Environments with Language-Conditioned Autotelic Agents

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Separating Hate Speech and Offensive Language Classes via Adversarial Debiasing

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Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions

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G rease V ision: Rewriting the Rules of the Interface

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Improving Generalization of Hate Speech Detection Systems to Novel Target Groups via Domain Adaptation

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`` Zo Grof ! '' : A Comprehensive Corpus for Offensive and Abusive Language in D utch

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Counter- TWIT : An I talian Corpus for Online Counterspeech in Ecological Contexts

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S tereo KG : Data-Driven Knowledge Graph Construction For Cultural Knowledge and Stereotypes

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The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists

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Lost in Distillation: A Case Study in Toxicity Modeling

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Cleansing & expanding the HURTLEX (el) with a multidimensional categorization of offensive words

Stamou, Vivian and Alexiou, Iakovi and Klimi, Antigone and Molou, Eleftheria and Saivanidou, Alexandra and Markantonatou, Stella. Cleansing & expanding the HURTLEX (el) with a multidimensional categorization of offensive words. Proceedings of the Sixth Workshop on Online Abuse...

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Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler

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Resources for Multilingual Hate Speech Detection

Arango Monnar, Ayme and Perez, Jorge and Poblete, Barbara and Salda \ n a, Magdalena and Proust, Valentina. Resources for Multilingual Hate Speech Detection. Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). 2022. doi:10.18653/v1/2022.woah-1.12

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Enriching Abusive Language Detection with Community Context

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DeTox: A Comprehensive Dataset for G erman Offensive Language and Conversation Analysis

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Multilingual H ate C heck: Functional Tests for Multilingual Hate Speech Detection Models

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Distributional properties of political dogwhistle representations in S wedish BERT

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Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions

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Accounting for Offensive Speech as a Practice of Resistance

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Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging

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Flexible text generation for counterfactual fairness probing

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Users Hate Blondes: Detecting Sexism in User Comments on Online R omanian News

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Targeted Identity Group Prediction in Hate Speech Corpora

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Revisiting Queer Minorities in Lexicons

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HATE - ITA : Hate Speech Detection in I talian Social Media Text

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Changes in Tweet Geolocation over Time: A Study with Carmen 2.0

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Extracting Mathematical Concepts from Text

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Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data

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Cross-Dialect Social Media Dependency Parsing for Social Scientific Entity Attribute Analysis

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Impact of Environmental Noise on A lzheimer ' s Disease Detection from Speech: Should You Let a Baby Cry?

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Exploring Multimodal Features and Fusion Strategies for Analyzing Disaster Tweets

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NTULM : Enriching Social Media Text Representations with Non-Textual Units

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Robust Candidate Generation for Entity Linking on Short Social Media Texts

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T rans POS : Transformers for Consolidating Different POS Tagset Datasets

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An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts

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Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification

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An Emotional Journey: Detecting Emotion Trajectories in D utch Customer Service Dialogues

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Supervised and Unsupervised Evaluation of Synthetic Code-Switching

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A rab G end: Gender Analysis and Inference on A rabic T witter

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Automatic Identification of 5 C Vaccine Behaviour on Social Media

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Automatic Extraction of Structured Mineral Drillhole Results from Unstructured Mining Company Reports

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`` Kanglish alli names! '' Named Entity Recognition for K annada- E nglish Code-Mixed Social Media Data

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Span Extraction Aided Improved Code-mixed Sentiment Classification

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A d BERT : An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements

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Increasing Robustness for Cross-domain Dialogue Act Classification on Social Media Data

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Disfluency Detection for V ietnamese

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A multi-level approach for hierarchical Ticket Classification

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Towards better structured and less noisy Web data: Oscar with Register annotations

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True or False? Detecting False Information on Social Media Using Graph Neural Networks

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Analyzing the Real Vulnerability of Hate Speech Detection Systems against Targeted Intentional Noise

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Proceedings of the 4th Workshop of Narrative Understanding (WNU2022). 2022

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Uncovering Surprising Event Boundaries in Narratives

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Compositional Generalization for Kinship Prediction through Data Augmentation

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How to be Helpful on Online Support Forums?

Wang, Zhilin and Torres, Pablo E. How to be Helpful on Online Support Forums?. Proceedings of the 4th Workshop of Narrative Understanding (WNU2022). 2022. doi:10.18653/v1/2022.wnu-1.3

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GPT -2-based Human-in-the-loop Theatre Play Script Generation

Rosa, Rudolf and Schmidtov \'a , Patr \' cia and Du s ek, Ond r ej and Musil, Tom \'a s and Mare c ek, David and Obaid, Saad and Nov \'a kov \'a , Marie and Voseck \'a , Kl \'a ra and Dole z al, Josef. GPT -2-based Human-in-the-loop Theatre Play Script Generation. Proceedings ...

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G is P y: A Tool for Measuring Gist Inference Score in Text

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Heroes, Villains, and Victims, and GPT -3: Automated Extraction of Character Roles Without Training Data

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Narrative Detection and Feature Analysis in Online Health Communities

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Looking from the Inside: How Children Render Character ' s Perspectives in Freely Told Fantasy Stories

van Duijn, Max and van Dijk, Bram and Spruit, Marco. Looking from the Inside: How Children Render Character ' s Perspectives in Freely Told Fantasy Stories. Proceedings of the 4th Workshop of Narrative Understanding (WNU2022). 2022. doi:10.18653/v1/2022.wnu-1.8

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Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion

Park, Seongmin and Lee, Jihwa. Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion. Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text. 2022. doi:10.18653/v1/2022.wit-1.1

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An Interactive Analysis of User-reported Long COVID Symptoms using T witter Data

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Tamire, Maunika and Anumasa, Srinivas and Srijith, P. K. Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification. Proceedings of the 2nd Workshop on Deriving Insights from User-Generated Text. 2022. doi:10.18653/v1/2022.wit-1.3

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Introducing EM - FT for M anipuri- E nglish Neural Machine Translation

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L 3 C ube- H ing C orpus and H ing BERT : A Code Mixed H indi- E nglish Dataset and BERT Language Models

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Leveraging Sub Label Dependencies in Code Mixed I ndian Languages for Part-Of-Speech Tagging using Conditional Random Fields

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H indi WSD : A package for word sense disambiguation in H inglish & H indi

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P \=a ṇinian Phonological Changes: Computation and Development of Online Access System

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L 3 C ube- M aha NER : A M arathi Named Entity Recognition Dataset and BERT models

Litake, Onkar and Sabane, Maithili Ravindra and Patil, Parth Sachin and Ranade, Aparna Abhijeet and Joshi, Raviraj. L 3 C ube- M aha NER : A M arathi Named Entity Recognition Dataset and BERT models. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and E...

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Identifying Emotions in Code Mixed H indi- E nglish Tweets

Sonu, Sanket and Haque, Rejwanul and Hasanuzzaman, Mohammed and Stynes, Paul and Pathak, Pramod. Identifying Emotions in Code Mixed H indi- E nglish Tweets. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Digital Accessibility and Information Mining of Dharma \'s \=a stric Knowledge Traditions

Nigam, Arooshi and Chandra, Subhash. Digital Accessibility and Information Mining of Dharma \'s \=a stric Knowledge Traditions. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Language Resource Building and E nglish-to-Mizo Neural Machine Translation Encountering Tonal Words

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Classification of Multiword Expressions in M alayalam

Cyriac, Treesa and Lalitha Devi, Sobha. Classification of Multiword Expressions in M alayalam. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Majumdar, Pritha and Alok, Deepak and Bansal, Akanksha and Ojha, Atul Kr. and McCrae, John P. B engali and M agahi PUD Treebank and Parser. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Makadi: A Large-Scale Human-Labeled Dataset for H indi Semantic Parsing

Vaibhav, Shashwat and Srivastava, Nisheeth. Makadi: A Large-Scale Human-Labeled Dataset for H indi Semantic Parsing. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Automatic Identification of Explicit Connectives in M alayalam

Sheeja S, Kumari and Lalitha Devi, Sobha. Automatic Identification of Explicit Connectives in M alayalam. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Web based System for Derivational Process of Kṛdanta based on P \=a ṇinian Grammatical Tradition

Sharma, Sumit and Chandra, Subhash. Web based System for Derivational Process of Kṛdanta based on P \=a ṇinian Grammatical Tradition. Proceedings of the WILDRE-6 Workshop within the 13th Language Resources and Evaluation Conference. 2022

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Computational Referencing System for S anskrit Grammar

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Attributing Fair Decisions with Attention Interventions

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Fraser, Kathleen C. and Kiritchenko, Svetlana and Balkir, Esma. Does Moral Code have a Moral Code? Probing Delphi ' s Moral Philosophy. Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022). 2022. doi:10.18653/v1/2022.trustnlp-1.3

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The Cycle of Trust and Responsibility in Outsourced AI

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Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers

Mosca, Edoardo and Harmann, Katharina and Eder, Tobias and Groh, Georg. Explaining Neural NLP Models for the Joint Analysis of Open-and-Closed-Ended Survey Answers. Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022). 2022. doi:10.18653/v...

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The Irrationality of Neural Rationale Models

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An Empirical Study on Pseudo-log-likelihood Bias Measures for Masked Language Models Using Paraphrased Sentences

Kwon, Bum Chul and Mihindukulasooriya, Nandana. An Empirical Study on Pseudo-log-likelihood Bias Measures for Masked Language Models Using Paraphrased Sentences. Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022). 2022. doi:10.18653/v1/2...

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Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models

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L 3 C ube- M aha H ate: A Tweet-based M arathi Hate Speech Detection Dataset and BERT Models

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Bias, Threat and Aggression Identification Using Machine Learning Techniques on Multilingual Comments

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The Role of Context in Detecting the Target of Hate Speech

Markov, Ilia and Daelemans, Walter. The Role of Context in Detecting the Target of Hate Speech. Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022). 2022

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Annotating Targets of Toxic Language at the Span Level

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Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning

Kirk, Hannah and Vidgen, Bertie and Hale, Scott. Is More Data Better? Re-thinking the Importance of Efficiency in Abusive Language Detection with Transformers-Based Active Learning. Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022). 2022

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A Lightweight Yet Robust Approach to Textual Anomaly Detection

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Detection of Negative Campaign in Israeli Municipal Elections

Litvak, Marina and Vanetik, Natalia and Talker, Sagiv and Machlouf, Or. Detection of Negative Campaign in Israeli Municipal Elections. Proceedings of the Third Workshop on Threat, Aggression and Cyberbullying (TRAC 2022). 2022

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Hypothesis Engineering for Zero-Shot Hate Speech Detection

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Multilevel Hypernode Graphs for Effective and Efficient Entity Linking

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Cross-Modal Contextualized Hidden State Projection Method for Expanding of Taxonomic Graphs

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Sharing Parameter by Conjugation for Knowledge Graph Embeddings in Complex Space

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A Clique-based Graphical Approach to Detect Interpretable Adjectival Senses in H ungarian

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The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge Injection

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Text-Aware Graph Embeddings for Donation Behavior Prediction

Dong, MeiXing and Xu, Xueming and Mihalcea, Rada. Text-Aware Graph Embeddings for Donation Behavior Prediction. Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing. 2022

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Word Sense Disambiguation of F rench Lexicographical Examples Using Lexical Networks

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R u DSI : Graph-based Word Sense Induction Dataset for R ussian

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Temporal Graph Analysis of Misinformation Spreaders in Social Media

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Proceedings of the Workshop on Terminology in the 21st century: many faces, many places. 2022

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Lexicon-driven approach for Terminology: specialized resources on the environment in B razilian P ortuguese

Lamberti Arraes, Fl \'a via. Lexicon-driven approach for Terminology: specialized resources on the environment in B razilian P ortuguese. Proceedings of the Workshop on Terminology in the 21st century: many faces, many places. 2022

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Knowledge Representation and Language Simplification of Human Rights

Silecchia, Sara and Vezzani, Federica and Di Nunzio, Giorgio Maria. Knowledge Representation and Language Simplification of Human Rights. Proceedings of the Workshop on Terminology in the 21st century: many faces, many places. 2022

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Converting from the Nordic Terminological Record Format to the TBX Format

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A Dataset for Term Extraction in H indi

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Terminology extraction using co-occurrence patterns as predictors of semantic relevance

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Evaluating Pre-Trained Language Models for Focused Terminology Extraction from S wedish Medical Records

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D -Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction

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Introducing the Digital Language Equality Metric: Technological Factors

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Collaborative Metadata Aggregation and Curation in Support of Digital Language Equality Monitoring

Giagkou, Maria and Piperidis, Stelios and Labropoulou, Penny and Deligiannis, Miltos and Kolovou, Athanasia and Voukoutis, Leon. Collaborative Metadata Aggregation and Curation in Support of Digital Language Equality Monitoring. Proceedings of the Workshop Towards Digital Lang...

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Measuring HLT Research Equality of E uropean Languages

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National Language Technology Platform for Public Administration

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Chang, Tyler A. and Bergen, Benjamin K. Word Acquisition in Neural Language Models. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00444

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Decomposing and Recomposing Event Structure

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F e T a QA : Free-form Table Question Answering

Nan, Linyong and Hsieh, Chiachun and Mao, Ziming and Lin, Xi Victoria and Verma, Neha and Zhang, Rui and Kry \'s ci \'n ski, Wojciech and Schoelkopf, Hailey and Kong, Riley and Tang, Xiangru and Mutuma, Mutethia and Rosand, Ben and Trindade, Isabel and Bandaru, Renusree and Cu...

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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets

Kreutzer, Julia and Caswell, Isaac and Wang, Lisa and Wahab, Ahsan and van Esch, Daan and Ulzii-Orshikh, Nasanbayar and Tapo, Allahsera and Subramani, Nishant and Sokolov, Artem and Sikasote, Claytone and Setyawan, Monang and Sarin, Supheakmungkol and Samb, Sokhar and Sagot, B...

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Clark, Jonathan H. and Garrette, Dan and Turc, Iulia and Wieting, John. Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00448

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Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations

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Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition

Geva, Mor and Wolfson, Tomer and Berant, Jonathan. Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00450

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Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation

Nishida, Noriki and Matsumoto, Yuji. Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00451

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Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages

Ramesh, Gowtham and Doddapaneni, Sumanth and Bheemaraj, Aravinth and Jobanputra, Mayank and AK, Raghavan and Sharma, Ajitesh and Sahoo, Sujit and Diddee, Harshita and J, Mahalakshmi and Kakwani, Divyanshu and Kumar, Navneet and Pradeep, Aswin and Nagaraj, Srihari and Deepak, K...

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Laban, Philippe and Schnabel, Tobias and Bennett, Paul N. and Hearst, Marti A. S umma C : Re-Visiting NLI -based Models for Inconsistency Detection in Summarization. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00453

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A Survey on Automated Fact-Checking

Guo, Zhijiang and Schlichtkrull, Michael and Vlachos, Andreas. A Survey on Automated Fact-Checking. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00454

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Predicting Document Coverage for Relation Extraction

Singhania, Sneha and Razniewski, Simon and Weikum, Gerhard. Predicting Document Coverage for Relation Extraction. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00456

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ABNIRML : Analyzing the Behavior of Neural IR Models

MacAvaney, Sean and Feldman, Sergey and Goharian, Nazli and Downey, Doug and Cohan, Arman. ABNIRML : Analyzing the Behavior of Neural IR Models. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00457

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Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference

Feng, Yufei and Yang, Xiaoyu and Zhu, Xiaodan and Greenspan, Michael. Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00458

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Dhingra, Bhuwan and Cole, Jeremy R. and Eisenschlos, Julian Martin and Gillick, Daniel and Eisenstein, Jacob and Cohen, William W. Time-Aware Language Models as Temporal Knowledge Bases. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00459

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Multilingual Autoregressive Entity Linking

De Cao, Nicola and Wu, Ledell and Popat, Kashyap and Artetxe, Mikel and Goyal, Naman and Plekhanov, Mikhail and Zettlemoyer, Luke and Cancedda, Nicola and Riedel, Sebastian and Petroni, Fabio. Multilingual Autoregressive Entity Linking. Transactions of the Association for Comp...

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B y T 5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models

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Designing an Automatic Agent for Repeated Language -- based Persuasion Games

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Towards General Natural Language Understanding with Probabilistic Worldbuilding

Saparov, Abulhair and Mitchell, Tom M. Towards General Natural Language Understanding with Probabilistic Worldbuilding. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00463

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A Multi-Level Optimization Framework for End-to-End Text Augmentation

Somayajula, Sai Ashish and Song, Linfeng and Xie, Pengtao. A Multi-Level Optimization Framework for End-to-End Text Augmentation. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00464

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Pruthi, Danish and Bansal, Rachit and Dhingra, Bhuwan and Soares, Livio Baldini and Collins, Michael and Lipton, Zachary C. and Neubig, Graham and Cohen, William W. Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?. Transactions of the Associatio...

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Shen, Zejiang and Lo, Kyle and Wang, Lucy Lu and Kuehl, Bailey and Weld, Daniel S. and Downey, Doug. VILA : Improving Structured Content Extraction from Scientific PDF s Using Visual Layout Groups. Transactions of the Association for Computational Linguistics. 2022. doi:10.116...

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Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Liu, Zoey and Prud ' hommeaux, Emily. Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00467

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PADA : Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Ben-David, Eyal and Oved, Nadav and Reichart, Roi. PADA : Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00468

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LOT : A Story-Centric Benchmark for Evaluating C hinese Long Text Understanding and Generation

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C zech Grammar Error Correction with a Large and Diverse Corpus

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T opi OCQA : Open-domain Conversational Question Answering with Topic Switching

Adlakha, Vaibhav and Dhuliawala, Shehzaad and Suleman, Kaheer and de Vries, Harm and Reddy, Siva. T opi OCQA : Open-domain Conversational Question Answering with Topic Switching. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00471

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A Neighborhood Framework for Resource-Lean Content Flagging

Sarwar, Sheikh Muhammad and Zlatkova, Dimitrina and Hardalov, Momchil and Dinkov, Yoan and Augenstein, Isabelle and Nakov, Preslav. A Neighborhood Framework for Resource-Lean Content Flagging. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tac...

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Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval

Geigle, Gregor and Pfeiffer, Jonas and Reimers, Nils and Vuli \'c , Ivan and Gurevych, Iryna. Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00473

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The F lores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation

Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc ' Aurelio and Guzm \'a n, Francisco and Fan, Angela. The F lores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine ...

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♫ M u S i Q ue: Multihop Questions via Single-hop Question Composition

Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish. ♫ M u S i Q ue: Multihop Questions via Single-hop Question Composition. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00475

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Relational Memory-Augmented Language Models

Liu, Qi and Yogatama, Dani and Blunsom, Phil. Relational Memory-Augmented Language Models. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00476

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Sentence Similarity Based on Contexts

Sun, Xiaofei and Meng, Yuxian and Ao, Xiang and Wu, Fei and Zhang, Tianwei and Li, Jiwei and Fan, Chun. Sentence Similarity Based on Contexts. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00477

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It ' s not Rocket Science: Interpreting Figurative Language in Narratives

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Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference

Li, Bangzheng and Yin, Wenpeng and Chen, Muhao. Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00479

2022 doi
[295]

Document Summarization with Latent Queries

Xu, Yumo and Lapata, Mirella. Document Summarization with Latent Queries. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00480

2022 doi
[296]

End-to-end Argument Mining with Cross-corpora Multi-task Learning

Morio, Gaku and Ozaki, Hiroaki and Morishita, Terufumi and Yanai, Kohsuke. End-to-end Argument Mining with Cross-corpora Multi-task Learning. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00481

2022 doi
[297]

and Ghosal, Atreya and Shrivastava, Manish and Singh, Maneesh and Srikumar, Vivek

Gupta, Vivek and Bhat, Riyaz A. and Ghosal, Atreya and Shrivastava, Manish and Singh, Maneesh and Srikumar, Vivek. Is My Model Using the Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning. Transactions of the Association for Computational Linguist...

2022 doi
[298]

Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression

Wang, Yuxia and Beck, Daniel and Baldwin, Timothy and Verspoor, Karin. Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00483

2022 doi
[299]

Data-to-text Generation with Variational Sequential Planning

Puduppully, Ratish and Fu, Yao and Lapata, Mirella. Data-to-text Generation with Variational Sequential Planning. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00484

2022 doi
[300]

True Few-Shot Learning with P rompts --- A Real-World Perspective

Schick, Timo and Sch. True Few-Shot Learning with P rompts --- A Real-World Perspective. Transactions of the Association for Computational Linguistics. 2022. doi:10.1162/tacl_a_00485

2022 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Davis and Paul Mermelstein , Journal =

Steven B. Davis and Paul Mermelstein , Journal =. Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences , Volume =

[2] [2]

Rabiner , Journal =

Lawrence R. Rabiner , Journal =. A Tutorial on Hidden

[3] [3]

The Elements of Statistical Learning -- Data Mining, Inference, and Prediction , Year =

Trevor Hastie and Robert Tibshirani and Jerome Friedman , Publisher =. The Elements of Statistical Learning -- Data Mining, Inference, and Prediction , Year =

[4] [4]

A really good paper about

Jane Smith and Firstname2 Lastname2 and Firstname3 Lastname3 , Pages =. A really good paper about. Proc

[5] [5]

An excellent paper introducing the

Robert Jones and Firstname2 Lastname2 and Firstname3 Lastname3 , Crossref =. An excellent paper introducing the

[6] [6]

Moore and Lucy Skidmore , title=

Roger K. Moore and Lucy Skidmore , title=. Proc

[7] [7]

Lessons Learned from Implementation of Voice Recognition for Documentation in the Military Electronic Health Record System , volume =

Hoyt, Robert and Yoshihashi, Ann , year =. Lessons Learned from Implementation of Voice Recognition for Documentation in the Military Electronic Health Record System , volume =

[8] [8]

The National medical journal of India , volume =

Ajami Sima , title =. The National medical journal of India , volume =