Pith. sign in

REVIEW 5 major objections 5 minor 27 references

Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A standard LSTM, trained on 1,200 sentence pairs, is reported to translate Sylheti to Modern Bangla at 89.3% accuracy.

desk verdict The Sylheti-to-Bangla translation task is real, but the 89.3% accuracy claim is undefined, unreproducible, and likely a training/classification metric, not translation quality. read the letter →

arxiv 2505.18709 v1 pith:NX7A7HHV submitted 2025-05-24 cs.CL cs.AI

Sourav Kumar Das , Md. Julkar Naeen , MD. Jahidul Islam , Md. Anisul Haque Sajeeb , Narayan Ranjan Chakraborty , Mayen Uddin Mojumdar This is my paper · ORCID
classification cs.CLcs.AI
keywords SylhetitranslationModernBanglaLSTMBi-LSTMSeq2Seqlow-resourceNLPdialectsneuralmachine
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

This paper tries to establish that a standard LSTM sequence model, trained on just 1,200 Sylheti–Modern Bangla sentence pairs, can translate the Sylheti dialect into Modern Bangla with a reported accuracy of 89.3 percent. The authors built a preprocessing pipeline, trained LSTM, Bi-LSTM, and Seq2Seq models, and found that the plain LSTM outperformed the other two on their evaluation. If the result holds, it would give low-resource Bangla dialect translation a simple baseline and a practical path to tools that help speakers of one Bangladeshi region understand another. The paper also frames the work as a step toward expanding Bangla NLP to local languages beyond the standard dialect.

What carries the argument

The central object is the LSTM as a token-level translator: Sylheti tokens are embedded into dense vectors, processed by a single LSTM layer with forget, input, and output gates, and projected to a vocabulary of 10,000 possible output tokens. The paper describes the standard LSTM gate equations and uses 50 epochs with batch size 32 on an 80/20 train-test split of the 1,200 pairs. The comparison architectures are a bidirectional LSTM with an added attention layer and an encoder–decoder Seq2Seq pair; the reported superiority of the plain LSTM is the mechanism the paper points to for why smaller recurrent models can work in this low-resource setting.

What would settle it

The claim would be settled by releasing the dataset and a fixed test split, then having independent Sylheti speakers evaluate the LSTM's outputs against their own reference translations; if the token-level match is not close to 0.8973, or if the published sample rows show the kind of corrupted Bengali characters seen in Table I, the reported result cannot be reproduced.

Watch

Extended reading notes

Core claim

On a dataset of 1,200 Modern Bangla sentences translated into Sylheti by speakers from the Sylhet region, an LSTM with 64-dimensional embeddings, 128 LSTM units, and a 10,000-token output vocabulary achieves the paper's best results: accuracy 0.8973, precision 0.8745, recall 0.8973, and F1 0.8838. The Bi-LSTM with attention reaches 0.7625 accuracy and the Seq2Seq encoder–decoder reaches 0.7069. The authors conclude that for this small parallel corpus, the plain LSTM is the best fit, and they attribute its advantage to a smaller train–validation gap than the other two models.

Load-bearing premise

The load-bearing premise is that the 1,200 Sylheti–Modern Bangla sentence pairs were correctly translated by Sylheti speakers and accurately aligned, since the reported 89.3 percent accuracy is computed entirely against this unreleased dataset.

Editorial extensions

If this is right

  • A 1,200-pair corpus is enough to make a plain LSTM the best of the three tested models, so future Sylheti translation work can start from this architecture instead of requiring large pretrained models.
  • The preprocessing pipeline of punctuation removal, stemming, padding, and tokenization can be reused for other Bangladeshi local languages such as Chittagong.
  • The authors state that scaling up the dataset and trying hybrid models are the natural next steps; their result implies data volume, not architecture, is the main bottleneck.
  • If the 89.3 percent figure transfers to new sentences, a simple LSTM-based application could give non-Sylheti speakers a usable translation tool for everyday sentences.

Reading between the lines

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

  • The reported 'accuracy' is a classification-style metric (TP/TN/FP/FN) applied to sequence generation; a standard machine-translation evaluation such as BLEU or human judgment would be needed to compare these numbers with other translation systems.
  • Because the metric counts tokens, a sentence with one wrong word is only partially counted; sentence-level exact-match accuracy is likely to be noticeably lower than 89.3 percent.
  • A testable extension would be releasing the 1,200-pair corpus plus a fixed test split, so the same LSTM can be re-run, scored by BLEU, and checked by independent Sylheti speakers.
  • The paper's future plan to include other regional languages could be helped by this same pipeline, but only if the collection and alignment protocol is documented and the data are shared.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper reports a comparative study of LSTM, Bi-LSTM, and Seq2Seq models for translating Sylheti Bangla to Modern Bangla using a private dataset of 1,200 sentence pairs. The authors describe data collection from Sylhet region speakers, preprocessing steps (punctuation removal, stemming, padding, tokenization), and model training with an 80/20 train/test split. The central claim, stated in the abstract and conclusion, is that LSTM achieves the best performance with 89.3% accuracy. The paper provides classification-style equations for accuracy, precision, recall, and F1, and a results table (Table III) listing scores for the three models.

Significance. If substantiated, a reliable Sylheti-to-Modern Bangla translator would be a useful contribution to low-resource Bangla dialect NLP, and the paper addresses a genuinely under-resourced language pair. However, the manuscript as written does not support its headline claim: the evaluation metric for a sequence-generation task is undefined, the reported numbers are internally inconsistent, and the dataset is neither released nor demonstrated to be reliable. These issues are load-bearing rather than cosmetic. The paper also provides no code, no ablation, and no statistical analysis, so the comparative ranking of the three architectures is not verifiable.

major comments (5)
  1. [Section IV, Eqs. (9)–(12), and Table III] The evaluation metric for the translation task is never defined. The paper applies classification formulas (accuracy, precision, recall, F1) based on TP/TN/FP/FN, but the task is variable-length sequence-to-sequence generation (LSTM max length 50; Seq2Seq input lengths 12 and 11). The paper does not state whether predictions are obtained by greedy decoding or teacher forcing, whether matches are exact sentence matches or token-level matches, how padding tokens are handled, or how out-of-vocabulary words are scored. Without this protocol, the reported 'accuracy' is not a well-defined measure of translation quality, and the headline number of 89.3% is unsupported.
  2. [Abstract vs. Section IV and Fig. 6 vs. Table III] The reported best accuracy is inconsistent across the paper: the abstract and conclusion state 89.3%, Section IV and Fig. 6 report 89.7%, and Table III reports 0.8973 (which is closer to 89.73%). These discrepancies, combined with the undefined metric, make it impossible to know which number is intended or how it was computed.
  3. [Section III.B.1 and Table I] The dataset is not released, and the sample rows in Table I contain apparently corrupted Bengali characters (e.g., 'আমি ত োিোকে শুনক পোচ্ছি নো' and 'েোগ েকেো নো'), which suggests either encoding errors in the manuscript or data of questionable integrity. The paper provides no annotation protocol, no inter-annotator agreement, and no details on how the Sylheti translations were verified. Since the entire reported accuracy is measured against this private dataset, these gaps undermine the validity of any quantitative claim.
  4. [Section IV and Table III] Table III reports Recall values numerically identical to Accuracy for all three models (0.8973, 0.7625, 0.7069). For any nontrivial sequence-generation task, this equality would be highly unusual and is not explained. The authors do not discuss the relationship between the two metrics, and the equality suggests that the scores may be copied, miscomputed, or derived from an inapplicable per-timestep classification setting rather than from actual translation evaluation.
  5. [Section III.C] The model descriptions are internally inconsistent and prevent reproduction. For example, the LSTM block states output shape (None, 50, 10000) and 2,767,632 parameters, while the Seq2Seq section says the dense output shape is 885 vectors for each token and later mentions a vector size of 1240 in the Bi-LSTM description. The text also gives contradictory numbers for vocabulary sizes and output dimensions. These inconsistencies, together with the lack of a clear decoding strategy, make it impossible to reconstruct the exact models that produced the reported results.
minor comments (5)
  1. [Section II] Several literature-review citations appear misnumbered: reference numbering in the text does not always match the listed references (e.g., [3] is described as sentiment analysis but the reference list entry for [3] is a Bangla-English NMT paper).
  2. [Section III.B.2] The stemming step is described as important, but no stemmer is specified or evaluated; the paper later admits there are not good stemmers for Bangla. It should either clarify which stemmer was used or omit the claim.
  3. [Section III.C] The phrase 'the outer space layer of our model is 128' is unclear; presumably it refers to LSTM hidden units, but the terminology is nonstandard and should be corrected.
  4. [Section IV] Figures 7–12 are referenced but not described in enough detail; the axis labels and units are not visible in the text, making the train/validation curves difficult to interpret.
  5. [Section V] The conclusion repeats the abstract's 89.3% figure, which conflicts with Section IV's 89.7%; the authors should reconcile these values and state the exact number with the evaluation protocol used.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported accuracy is an internal held-out evaluation of three models trained on the authors' own dataset, not a derivation that reduces to its inputs.

full rationale

The paper's derivation chain is empirical rather than formal: collect 1,200 Sylheti to Modern Bangla sentence pairs, preprocess them, split 80/20, train LSTM, Bi-LSTM, and Seq2Seq models, and report held-out classification-style metrics in Table III. The LSTM result (0.8973 in Table III, described as 89.3% in the abstract) is measured on the 20% test portion of the same private dataset. This is a standard model-comparison procedure and does not exhibit any of the circularity patterns: no metric or model definition is constructed in terms of the target accuracy; no parameter is fitted to the test set and then renamed as a prediction; and the paper does not rely on self-citations for its central claim. The main validity concerns are that the accuracy metric is undefined for variable-length sequence-to-sequence translation (equations 9-12 are classification metrics, no decoding or alignment rule is specified), the dataset is not released, and Table I shows apparently corrupted Bengali characters. These are correctness and reproducibility risks, not circularity. Because the claim is not equivalent to its inputs by construction, the circularity score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on an unvalidated 1,200-sentence parallel dataset, an underdefined accuracy metric, and hand-chosen hyperparameters. No new theoretical entities are introduced; the only asserted numbers are the fitted model parameters and their reported test-set performance.

free parameters (4)
  • LSTM embedding dimension and LSTM hidden units = Embedding 64, hidden 128
    Section III.C LSTM model; chosen by hand, no justification; affects model capacity and reported accuracy.
  • Bi-LSTM and Seq2Seq embedding and hidden dimensions = Embedding 256, hidden 256 (Seq2Seq), hidden 512 total (Bi-LSTM)
    Section III.C; chosen by hand, no justification.
  • Training schedule = 50 epochs for all; batch sizes 32 (LSTM, Seq2Seq) and 64 (Bi-LSTM)
    Section III.C; no early stopping, risks overfitting on 960 training sentences.
  • Sequence length caps and vocabulary size = Max lengths 50 (LSTM), 15 (Bi-LSTM), 12 (Seq2Seq); vocabulary 10,000
    Section III.C; these caps define padding/truncation and output dimensions; not shown to be data-driven.
assumptions (5)
  • standard math The LSTM gate equations (Eq. 2-6), as implemented in TensorFlow/Keras, correctly model sequential dependencies in the text.
    The paper relies on the standard LSTM formulation without proof or verification; this is accepted background knowledge.
  • domain assumption The 1,200 Modern Bangla-Sylheti sentence pairs are correct, aligned, and representative of the Sylheti dialect.
    Section III.B.1 states the data was collected from citizens of Sylhet, but no annotation protocol or quality check is described. Sample rows in Table I show corrupted-looking Bengali text, so this assumption is fragile.
  • domain assumption The accuracy formula in Eq. (9) is a meaningful measure of translation quality for the model's sequence outputs.
    Eq. (9) defines classification accuracy, but the paper does not specify how token-level predictions are compared with reference translations, so the metric is underdefined.
  • domain assumption The 80/20 train/test split is random and representative of the full distribution.
    Section III.B.2 states the split without details on stratification or randomness; with only 240 test sentences, accuracy has high variance and may be non-representative.
  • domain assumption The preprocessing steps of stemming and tokenization are applied correctly and consistently to the Bengali text.
    Section III.B.2 lists these steps but also notes the lack of good Bangla stemmers; the paper does not name the tools or verify their output.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla." pith.science (2026). https://pith.science/paper/NX7A7HHV

@misc{pith2026250518709,
  author       = {Pith},
  title        = {Pith review of: Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NX7A7HHV}},
  note         = {Machine review of arXiv:2505.18709}
}
read the original abstract

Bangla or Bengali is the national language of Bangladesh, people from different regions don't talk in proper Bangla. Every division of Bangladesh has its own local language like Sylheti, Chittagong etc. In recent years some papers were published on Bangla language like sentiment analysis, fake news detection and classifications, but a few of them were on Bangla languages. This research is for the local language and this particular paper is on Sylheti language. It presented a comprehensive system using Natural Language Processing or NLP techniques for translating Pure or Modern Bangla to locally spoken Sylheti Bangla language. Total 1200 data used for training 3 models LSTM, Bi-LSTM and Seq2Seq and LSTM scored the best in performance with 89.3% accuracy. The findings of this research may contribute to the growth of Bangla NLP researchers for future more advanced innovations.

Figures

Figures reproduced from arXiv: 2505.18709 by the authors.

Figure 2
Figure 2. Diagram of order of the data processing steps It has been made sure that the collected data or the sentences are not consisting of any Non character letter, non-Bengali character or any kind of duplicate values. 80% data used for training the model and the rest of the 20% used in testing the trained system [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. shows the input, forward layer, backward layer, outputs [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Sequence to Sequence model architecture IV. Result Analysis & Discussion The research was conducted using 1200 sentences and contains two columns named ‘Bangla’ and ‘Sylheti’. The models are built with Keras, tensor flow and python. A free cloud named Jupyter Notebook was used to execute the codes. To calculate the accuracy, f1 score, precision and recall this formula were applied: Accuracy: The accuracy of a model … view at source ↗
Figures from the paper (1 more)
Figure 7
Figure 7. Figure 7: Fig. 8. & Fig. 9. Shows the train and validation [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

27 extracted references · 26 canonical work pages

  1. [1]

    Dataset consists of a total 1200 Modern Bangla sentences that are translated to Sylheti Bangla language

    Data Collection: The data is collected from the citizens of Sylhet region. Dataset consists of a total 1200 Modern Bangla sentences that are translated to Sylheti Bangla language. Sylheti locally spoken Bangla is different from the actual Bangla. So, the dataset has tw o attributes Modern Bangla which is the actual Bangla language and Sylheti Bangla that ...

    work page

  2. [2]

    Data Preprocessing: Fig. 2. Diagram of order of the data processing steps It has been made sure that the collected data or the sentences are not consisting of any Non character letter, non-Bengali character or any kind of duplicate values. 80% data used for training the model and the rest of the 20% used in testing the trained system. Fig. 2. Shows the da...

    work page

  3. [3]

    and Kamal, M.A.S.,

    Roy, A., Dhar, A.C., Akhand, M.A.H. and Kamal, M.A.S.,

    work page

  4. [4]

    English to Bangla Machine Translation Using Recurrent Neural Network

    Siddique, S., Ahmed, T., Talukder, M.R.A. and Uddin, M.M., 2021. English to bangla machine translation using recurrent neural network. arXiv preprint arXiv:2106.07225

    work page Pith review arXiv 2021

  5. [5]

    NusaX: Multilingual Parallel Sentiment Dataset for 10 Indonesian Local Languages

    Winata, G.I., Aji, A.F., Cahyawijaya, S., Mahendra, R., Koto, F., Romadhony , A., Kurniawan, K., Moeljadi, D., Prasojo, R.E., Fung, P. and Baldwin, T., 2022. NusaX: Multilingual parallel sentiment dataset for 10 Indonesian local languages. arXiv preprint arXiv:2205.15960

    work page Pith review arXiv 2022

  6. [6]

    and Rahman, M.M., 2022

    Wadud, M.A.H., Mridha, M.F. and Rahman, M.M., 2022. Word embedding methods for word representation in deep learning for natural language processing. Iraqi Journal of Science, pp.1349-1361

    work page 2022

  7. [7]

    and Kar, S.,

    Hossain, M.Z., Rahman, M.A., Islam, M.S. and Kar, S.,

    work page

  8. [8]

    Sentiment analysis in Bengali Text using NLP

    Sarkar, Andkon, Aishwarja Paul Sourav, and Rezvi Ahmed. "Sentiment analysis in Bengali Text using NLP." PhD diss., Brac University, 2023

    work page 2023

Show all 27 references
  1. [9]

    Natural language processing and machine learning based cyberbullying detection for Bangla and Romanized Bangla texts

    Ahmed, Md Tofael, et al. "Natural language processing and machine learning based cyberbullying detection for Bangla and Romanized Bangla texts." TELKOMNIKA (Telecommunication Computing Electronics and Control) 20.1 (2021): 89-97

    2021

  2. [10]

    and Khushbu, S.A., 2023

    Das, R.K., Islam, M. and Khushbu, S.A., 2023. BTSD: A curated transformation of sentence dataset for text classification in Bangla language. Data in Brief , 50, p.109445

    2023

  3. [11]

    Finally, the dense layer is connected with lstm_1 0,0 with 227,445 parameters

    The hidden state size for both is 256. Finally, the dense layer is connected with lstm_1 0,0 with 227,445 parameters. This applies linear transformation for the final output and the output shape is 885 vectors for each token in the given sentence. The max length is 12 and the ...

  4. [12]

    and Hasan, M.M., 2021

    Sayeed, A., Shin, J., Hasan, M.A.M., Srizon, A.Y. and Hasan, M.M., 2021. Bengalinet: A low -cost novel convolutional neural network for bengali handwritten characters recognition. Applied Sciences, 11(15), p.6845

    2021

  5. [13]

    and Medhat, W., 2022

    Fouad, K.M., Sabbeh, S.F. and Medhat, W., 2022. Arabic Fake News Detection Using Deep Learning. Computers, Materials & Continua, 71(2)

    2022

  6. [14]

    and Mondal, M.R.H.,

    Bhowmik, N.R., Arifuzzaman, M. and Mondal, M.R.H.,

  7. [15]

    and Al Hasan, S., 2020, August

    Hussain, M.G., Hasan, M.R., Rahman, M., Protim, J. and Al Hasan, S., 2020, August. Detection of bangla fake news using mnb and svm classifier. In 2020 International Conference on Computing, Electronics & Communications Engineering (iCCECE) (pp. 81 -85). IEEE

    2020

  8. [16]

    and Fahmin, A., 2020, December

    Mugdha, S.B.S., Ferdous, S.M. and Fahmin, A., 2020, December. Evaluating machine learning algorithms for bengali fake news detection. In 2020 23rd International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE

    2020

  9. [17]

    and Islam, M.S., 2021

    Bhowmik, N.R., Arifuzzaman, M., Mondal, M.R.H. and Islam, M.S., 2021. Bangla text sentiment analysis using supervised machine learning with extended lexicon dictionary. Natural Language Processing Research , 1(3- 4), pp.34-45

    2021

  10. [18]

    and Roy, P., 2021, September

    Sen, O. and Roy, P., 2021, September. A convolutional neural network based approach to recognize Bangla spoken digits from speech signal. In 2021 International Conference on Electronics, Communications and Information Technology (ICECIT) (pp. 1-4). IEEE

    2021

  11. [19]

    and Mandal, C.K., 2023

    Roy, A., Sarkar, K. and Mandal, C.K., 2023. Bengali Text Classification: A New multi -class Dataset and Performance Evaluation of Machine Learning and Deep Learning Models

    2023

  12. [20]

    and Chakraborty, P., 2021, May

    Rahman, S. and Chakraborty, P., 2021, May. Bangla document classification using deep recurrent neural network with BiLSTM. In Proceedings of International Conference on Machine Intelligence and Data Science Applications: MIDAS 2020 (pp. 507 -519). Singapore: Springer Singapore

    2021

  13. [21]

    and Ashraf, F.B., 2021, December

    Salehin, K., Alam, M.K., Nabi, M.A., Ahmed, F. and Ashraf, F.B., 2021, December. A comparative study of different text classification approaches for bangla news classification. In 2021 24th International Conference on Computer and Information Technology (ICCIT) (pp. 1-6). IEEE

    2021

  14. [23]

    and Zahra, A., 2022

    Wijonarko, P. and Zahra, A., 2022. Spoken language identification on 4 Indonesian local languages using deep learning. Bulletin of Electrical Engineering and Informatics, 11(6), pp.3288-3293

    2022

  15. [25]

    and Rahman, M.M., 2022, October

    Hasan, M., Puja, S.P., Bijoy, M.H.I., Sattar, A. and Rahman, M.M., 2022, October. Multiple Bangla Sentence Classification using Machine Learning and Deep Learning Algorithms. In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) ...

    2022

  16. [26]

    and Rahman, A., 2022, February

    Alam, S., Haque, M.A.U. and Rahman, A., 2022, February. Bengali text categorization based on deep hybrid CNN –LSTM network with word embedding. In 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET) (pp. 577 -582). IEEE

    2022

  17. [2020]

    arXiv preprint arXiv:2004.08789

    Banfakenews: A dataset for detecting fake news in bangla. arXiv preprint arXiv:2004.08789

    2004 arXiv

  18. [2021]

    Bangla -english neural machine translation with bidirectional long short -term memory and back translation. Int. J. Comput. Vis. Signal Process , 11(1), pp.25-31

  19. [2022]

    Array, 13, p.100123

    Sentiment analysis on Bangla text using extended lexicon dictionary and deep learning algorithms. Array, 13, p.100123

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.