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REVIEW 4 major objections 5 minor 26 references

Cross-Language Approach for Quranic QA

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that translating Arabic Quran questions into English and fine-tuning pre-trained language models on the expanded English dataset significantly improves Quranic passage retrieval, with RoBERTa-Base reaching the best…

desk verdict A well-organized but under-controlled extension of known translation-based Quranic QA; the headline claim is confounded by an undefined baseline and bundled changes, so the paper needs major revision before its conclusions can be trusted. read the letter →

arxiv 2501.17449 v1 pith:ZY4DMICH submitted 2025-01-29 cs.CL cs.IR

Islam Oshallah , Mohamed Basem , Ali Hamdi , Ammar Mohammed This is my paper · ORCID
classification cs.CLcs.IR
keywords QuranQuestionAnsweringPassageRetrievalModernStandardArabicClassicalDatasetExpansionFine-TuningCross-LanguageApproach
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 proposes a cross-language recipe for Quranic question answering: machine-translate the Arabic questions into English, paraphrase each question to multiply the data, retrieve answers from an English translation of the Quran, and fine-tune English-optimized transformer models on top. The authors report that this recipe clearly raises passage-retrieval scores over the base versions of the same models, with RoBERTa-Base reaching MAP@10 of 0.34 and MRR of 0.52 and DeBERTa-v3-Base reaching Recall@10 of 0.50 and Precision@10 of 0.24. The motivation is to sidestep the gap between Modern Standard Arabic questions and Classical Arabic Quranic verses by moving the whole task into English, where stronger pre-trained models are available. A sympathetic reader would take this as evidence that cross-language transfer is a practical path for low-resource scriptural QA.

What carries the argument

The mechanism carrying the argument is the cross-language data pipeline: Arabic questions are machine-translated, paraphrased twice for linguistic diversity, and paired with passages from an English translation of the Quran, so that English-optimized transformer models can be fine-tuned as cross-encoders—networks that take a question and a passage as a single input and emit a relevance score. That pipeline converts 251 original questions into 1,895 English training items. The paper's conclusion is carried by the 'Base' versus 'Ours' comparison in Table 2, where 'Ours' bundles translation, dataset expansion, and SQuAD v2 pre-fine-tuning.

What would settle it

Run the same 1,895-question expanded dataset entirely in Arabic—Arabic questions, an Arabic Quran text, and an Arabic cross-encoder—and compare MAP@10 and MRR with the English pipeline; if the Arabic run matches or beats the English numbers, translation is not the active ingredient. A complementary test would translate the questions but keep the original 251-question dataset, isolating translation from dataset expansion.

Watch

Extended reading notes

Core claim

The central claim, in the authors' terms, is that the combination of dataset expansion and cross-language retrieval—growing the original 251-question shared-task set to 1,895 paraphrased English questions, using an English translation of the Quran as the passage corpus, pre-fine-tuning on SQuAD v2, and then fine-tuning seven transformer models as cross-encoders—improves performance over each model's baseline on every metric reported. In their results, ELECTRA-Large shows the largest relative jump (MAP@10 from 0.04 to 0.31), RoBERTa-Base gives the best ranking (MAP@10 0.34, MRR 0.52), and DeBERTa-v3-Base gives the best coverage (Recall@10 0.50, Precision@10 0.24). The authors interpret these gains as showing that English-language models can overcome the MSA-to-Classical Arabic linguistic divide.

Load-bearing premise

The conclusion depends on the 'Base' versus 'Ours' comparison in Table 2 being a valid isolation of the cross-language approach, but 'Ours' changes the dataset size, the language, and the pre-training step all at once, and the paper never defines what 'Base' means.

Editorial extensions

If this is right

  • Teams working on Quranic QA can adopt the expanded 1,895-question English dataset and cross-encoder setup as a ready baseline instead of building Arabic-specific systems from scratch.
  • RoBERTa-Base is the recommended model when ranking quality matters most, since it attains the highest MAP@10 (0.34) and MRR (0.52).
  • DeBERTa-v3-Base is the recommended model when coverage matters most, with Recall@10 of 0.50 and Precision@10 of 0.24.
  • The reported gains imply that even large 7-billion-parameter models (Bloom, Falcon) benefit from the translated dataset, suggesting that resource scale does not remove the need for cross-language data preparation.

Reading between the lines

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

  • A natural next step the authors leave implicit is an ablation that separates translation from dataset expansion; without it, the reported gains cannot be attributed specifically to the cross-language choice.
  • The same translate-and-fine-tune recipe could be tested on other classical-language corpora, such as hadith or medieval Arabic texts, where a high-resource target language might unlock otherwise unavailable retrieval quality.
  • A hybrid system that uses the English model for ranking but maps passages back to the original Arabic verses would preserve access to the Arabic text while keeping the ranking gains; the paper stops at the English translation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a cross-language approach to Quranic question answering. The authors expand a small Arabic QA dataset (251 questions) into 1,895 English questions via external resources, paraphrasing, and Google Translate, retrieve answers from Pickthall's English translation of the Quran, and fine-tune several language models and large language models with a cross-encoder architecture. They report evaluation results on MAP@10, MRR, Recall@10, and Precision@10, claiming that the cross-language approach significantly improves performance, with RoBERTa-Base achieving the highest MAP@10 (0.34) and MRR (0.52), and DeBERTa-v3-Base excelling in Recall@10 (0.50) and Precision@10 (0.24). However, the paper's central comparison in Table 2 (Base vs. Ours) is not interpretable because 'Base' is never defined and the 'Ours' condition bundles several simultaneous changes, so the central claim is not supported by the reported experiments as written.

Significance. If the claimed improvements were properly isolated, the work could be a useful contribution to low-resource Quranic QA, an important domain for a large population of users. The paper addresses a genuine linguistic challenge (MSA questions versus Classical Arabic passages), evaluates multiple models, and uses standard retrieval metrics. The main strength is the breadth of the experimental effort. However, the current experimental design does not allow one to attribute the reported gains to the cross-language component: there is no defined baseline, no ablation, and no control condition. The also-important issue of whether the test set overlaps with the expanded training set is left unexplained. As a result, the paper's central claim is not yet verified, and the practical significance of the numbers is unclear without comparison to prior published results on the same test set.

major comments (4)
  1. [Section 3.1-3.3, Table 2] The 'Base' condition in Table 2 is never defined. The caption says 'baseline (Base)' but the text provides no description of what Base is (e.g., zero-shot, Arabic-input, SQuAD-only, or a shared-task baseline). At the same time, the 'Ours' condition changes four variables jointly: dataset size increases from 251 to 1,895 questions, questions are translated from Arabic to English via Google Translate, the answer corpus changes from the Arabic Quran to Pickthall's English translation, and models are pre-fine-tuned on SQuAD v2 before Quran QA fine-tuning. Because the experiment does not vary these factors independently, the improvement from Base to Ours cannot be attributed specifically to the cross-language approach, which is the paper's central claim. The authors should define Base explicitly and run ablations (e.g., Arabic-only with the expanded dataset and the same fine-tuning schedule) to isolate the effect of translation.
  2. [Section 3.1 and Section 4] The paper never states how the 1,895-question expanded dataset is split into training, development, and test sets. The original Quran QA 2023 dataset has 174 training, 52 test, and 25 development questions. If any of the 52 original test questions were included in the expansion and used during fine-tuning, the reported scores in Table 2 would be optimistic and potentially invalid. The authors must specify exactly which questions were used for the evaluation in Table 2 and confirm that no test question, original or expanded, was present in the training data.
  3. [Section 4, Results] The paper reports only the relative improvements of 'Ours' over the undefined 'Base' and does not compare against previously published results on the same Quran QA 2023 shared task. For example, related work in Section 2 mentions a MAP of 0.25 by Elkomy and Sarhan; the paper's own DeBERTa-v3-Base achieves MAP@10 of 0.32, but it is unclear whether these numbers are comparable because the evaluation protocols (test sets, metrics, and passage pools) are not stated to be the same. To demonstrate practical significance, the authors should evaluate their models under the official shared-task setting or at least directly compare with prior systems on the same test questions and metrics.
  4. [Section 3.3 and 3.4] The training procedure is underspecified, making the results difficult to reproduce or assess. The paper does not report hyperparameters (learning rate, batch size, number of epochs), the number of negative passages per question, the loss function, or how the cross-encoder is implemented (e.g., which model serves as the encoder, how positive and negative passages are sampled). The statement in Section 3.4 that 'contrastive learning could further help' is speculative and not actually implemented, adding to the ambiguity. These details are necessary for confidence in the reported numbers.
minor comments (5)
  1. [Section 1, last paragraph] The phrase 'Fine-tuned LLMs, such as BERT-Medium' is inaccurate because BERT-Medium is a small language model, not a large language model; the authors should consistently use 'LMs and LLMs' throughout.
  2. [Table 1] Bloom is listed with 7 billion parameters, but the cited reference is for the 176B-parameter Bloom; if a smaller Bloom variant was used, the reference should be updated and the model checkpoint should be specified.
  3. [Figure 2 caption] The caption says the retrieved passage is 'translated back into Arabic,' but no back-translation step is described in the methodology or shown to be part of the evaluation; the authors should either clarify or remove this step from the figure.
  4. [Section 4 and Abstract] The notation for the metrics is inconsistent: 'MAP10' in the table and results text versus 'MAP@10' in the abstract; the same applies to Recall and Precision. Please unify the notation.
  5. [References] Reference [15] is cited as a HuggingFace dataset URL rather than a proper citation to the SQuAD v2 paper; the citation should be completed with the original reference.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: reported gains are empirical comparisons; the main weakness is an unisolated confound, not circularity.

full rationale

This is an empirical systems paper, not a derivation. The central claim that the cross-language pipeline improves Quranic QA retrieval is supported by Table 2, which compares Base and Ours scores. I checked the standard circularity patterns. First, no metric is fitted and then renamed as a prediction: MAP@10, MRR, Recall, and Precision are computed from ranked outputs of fine-tuned models, and the paper does not define any parameter in terms of a target value. Second, there is no load-bearing self-citation, no imported uniqueness theorem, and no ansatz smuggled in via citation; the related work explicitly credits Alawwad et al. [13] with the translation-based retrieval idea, so the cross-language step is an adopted intervention rather than a rename of a known result. Third, the claim is not self-definitional: translating questions to English and using Pickthall's English Quran is an experimental manipulation, not an equation that equals its own output. The main validity threat is that Table 2's Ours condition simultaneously expands the dataset, translates questions, changes the answer corpus to English, and pre-fine-tunes on SQuAD v2, while Base is never defined; Section 4's statement that the table 'highlights the improvements achieved by the fine-tuned models' and Section 5's attribution of gains to 'cross-language strategies' therefore over-claim causal isolation. This is an experimental confound / identification problem, not circularity by construction. No specific reduction of an equation or fitted parameter to its input can be exhibited, so the circularity score is 0.

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

The paper introduces no analytical free parameters; all reported values are empirical retrieval scores. The core assumptions are domain-level: that machine translation preserves meaning, that the chosen English Quran translation is reliable, that the assembled relevance labels are correct, and that the Base/Ours comparison provides a valid control. No new physical or mathematical entities are introduced.

assumptions (4)
  • domain assumption Google Translate preserves the meaning of Arabic Quran questions well enough for English retrieval to work.
    Invoked in Section 3.2, where questions are translated with the Google Translate API; no translation quality check is reported.
  • domain assumption Marmaduke Pickthall's English translation of the Quran is an accurate and faithful representation of the Quranic text.
    Invoked in Section 3.2 as the source of answers; its authenticity is asserted but not evaluated.
  • domain assumption The expanded dataset's question-passage relevance labels are correct despite being assembled from heterogeneous sources without validation.
    Section 3.1 describes collecting from shared task data, PDFs, and a plant list, with cleaning but no label verification or inter-annotator agreement.
  • ad hoc to paper The Base versus Ours comparison in Table 2 is a valid control for cross-language effects.
    The paper treats the comparison as evidence for cross-language benefit, but the conditions differ in dataset size and SQuAD pre-fine-tuning as well, so this assumption is not justified.

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Cite this review

Pith. "Pith review of Cross-Language Approach for Quranic QA." pith.science (2026). https://pith.science/paper/ZY4DMICH

@misc{pith2026250117449,
  author       = {Pith},
  title        = {Pith review of: Cross-Language Approach for Quranic QA},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZY4DMICH}},
  note         = {Machine review of arXiv:2501.17449}
}
read the original abstract

Question answering systems face critical limitations in languages with limited resources and scarce data, making the development of robust models especially challenging. The Quranic QA system holds significant importance as it facilitates a deeper understanding of the Quran, a Holy text for over a billion people worldwide. However, these systems face unique challenges, including the linguistic disparity between questions written in Modern Standard Arabic and answers found in Quranic verses written in Classical Arabic, and the small size of existing datasets, which further restricts model performance. To address these challenges, we adopt a cross-language approach by (1) Dataset Augmentation: expanding and enriching the dataset through machine translation to convert Arabic questions into English, paraphrasing questions to create linguistic diversity, and retrieving answers from an English translation of the Quran to align with multilingual training requirements; and (2) Language Model Fine-Tuning: utilizing pre-trained models such as BERT-Medium, RoBERTa-Base, DeBERTa-v3-Base, ELECTRA-Large, Flan-T5, Bloom, and Falcon to address the specific requirements of Quranic QA. Experimental results demonstrate that this cross-language approach significantly improves model performance, with RoBERTa-Base achieving the highest MAP@10 (0.34) and MRR (0.52), while DeBERTa-v3-Base excels in Recall@10 (0.50) and Precision@10 (0.24). These findings underscore the effectiveness of cross-language strategies in overcoming linguistic barriers and advancing Quranic QA systems

Figures

Figures reproduced from arXiv: 2501.17449 by the authors.

Figure 1
Figure 1. illustrates the workflow adopted in this study, showcasing the process from dataset augmentation to fine-tuning and evaluation. This diagram highlights the cross-language approach and the transformations of the data set essential for improving the Quranic QA system. Old Dataset 174 Q Train 52 Q Test 25 Q Dev Augment with Literature & Paraphrase Expanded Dataset: 1895 Q (Arabic) Translate to English Final Dataset: 18… view at source ↗
Figure 2
Figure 2. Cross-Language Example of Quranic QA: A question posed in Arabic is translated into English by using Google Translate API,then used to retrieve relevant passages from the English-translated Quran, and then translated back into Arabic [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Workflow for LLM & LM training 3. Randomizer and Combined Data: The system combines both positive and negative passages using a randomizer . After mixing positive and negative passages with a randomizer, contrastive learning could further help the model by creating training pairs. Each pair would be evaluated to ensure that the model distinguishes between relevant and irrelevant passages effectively. This would enha… view at source ↗

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Reference graph

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Reviewed August 10, 2026 · model on record in the stance chip above.