Cross-Lingual Machine Reading Comprehension

Bing Qin; Guoping Hu; Shijin Wang; Ting Liu; Wanxiang Che; Yiming Cui

REVIEW 4 major objections 5 minor 23 references

Cross-Lingual Machine Reading Comprehension

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Cross-lingual transfer through a shared multilingual BERT lets English reading-comprehension data improve Chinese machine reading comprehension, and the proposed Dual BERT achieves state-of-the-art results on CMRC 2018 and DRCD…

desk verdict A solid, honest empirical study of cross-lingual MRC with real gains on Chinese benchmarks; the central bilingual-attention mechanism is asserted rather than proven, but the paper is worth refereeing. read the letter →

arxiv 1909.00361 v1 pith:BE7X5I2D submitted 2019-09-01 cs.CL cs.LGcs.NE

Yiming Cui , Wanxiang Che , Ting Liu , Bing Qin , Shijin Wang , Guoping Hu This is my paper · ORCID

classification cs.CLcs.LGcs.NE

keywords cross-lingualmachinereadingcomprehensionDualBERTmultilingualback-translationspanextractionlow-resourcelanguagesself-adaptiveattentiontranslation

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 tries to show that machines can learn to read in a low-resource language by importing skill from a high-resource language, even when the languages are very different. It formalizes a Cross-Lingual Machine Reading Comprehension task, gives back-translation pipelines that translate passages and questions into English, answer them there, and map the answers back, and then proposes Dual BERT, which reads both the Chinese sample and its English translation in one shared multilingual BERT and decodes the Chinese answer from the bilingual context. The results on two public Chinese datasets show consistent gains over systems trained only on Chinese, with state-of-the-art scores and a human-beating exact-match score on one benchmark. If this holds, languages with little labeled reading-comprehension data can leverage English SQuAD-scale data through translation plus a shared representation.

What carries the argument

The load-bearing mechanism is the Self-Adaptive Attention in the bilingual decoder: it multiplies the raw inter-lingual attention $A_{TS}=B_T B_S^\top$ on both sides by the self-attention maps $A_T=\mathrm{softmax}(B_T B_T^\top)$ and $A_S=\mathrm{softmax}(B_S B_S^\top)$, producing $\tilde A_{TS}=A_T A_{TS} A_S^\top$, and then attends to source representations with it. This lets each language first filter its own irrelevant tokens and then lets target tokens query the filtered source context. A cosine-similarity weight $\lambda=\max\{0,\cos\langle \tilde H_S,\tilde H_T\rangle\}$ on the auxiliary source-span loss down-weights translated samples whose ground-truth span sits far from the target span in representation space.

What would settle it

Run Dual BERT on a language pair with poor multilingual-BERT alignment (for example, English and a typologically distant language with little shared vocabulary) and check whether the bilingual decoder's gain over cascade training disappears or reverses; if the shared embedding space cannot align the two languages, the transfer mechanism fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that cross-lingual transfer for extractive reading comprehension works by decoding target spans through a shared multilingual BERT that simultaneously sees the machine-translated source version of the same passage-question pair. Dual BERT uses the target BERT representation as the query and the source representation as key and value in a modified multi-head attention layer, then fuses the attended source representation into the target representation with a residual connection. The auxiliary source-language span loss is weighted by a dynamic lambda, computed as the cosine similarity between the ground-truth span representations in the two languages, so noisy translations contribute less. On CMRC 2018 and DRCD the model improves over target-only multilingual BERT, cascade training, and mixed English-Chinese training, and it surpasses human exact-match on the DRCD test set.

Load-bearing premise

The load-bearing premise is that multilingual BERT's embedding space aligns English and Chinese tokens well enough for the bilingual decoder's cross-lingual attention to transfer span information; if that token-level alignment fails, the model cannot transfer reading-comprehension skill from source to target.

Editorial extensions

If this is right

Dual BERT with SQuAD pre-training sets state-of-the-art results on CMRC 2018 and DRCD, including a human-beating exact-match score on DRCD test.
The character-level SimpleMatch alignment improves zero-shot Japanese and French SQuAD results, raising Japanese F1 by 11.8 points, more than the prior NMT-attention alignment method.
Back-translation with an Answer Verifier surpasses multilingual BERT trained only on target-language data, so translated English samples can strengthen supervised Chinese MRC.
When source pre-training data is plentiful, using a distant but data-rich language (English) can work as well as or better than using a close but data-poor language (Traditional Chinese) for a Simplified Chinese task.

Reading between the lines

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

The same recipe should extend to other language pairs covered by multilingual BERT and a decent translation system, but the Japanese and French results are zero-shot baselines only, so the full Dual BERT training loop on those languages is untested.
The dynamic lambda could be reused as a general noise-weighting signal for any machine-translated training data, since it measures whether source and target representations of the same answer are close.
The discussion's conclusion that data size beats language proximity, if right, suggests a practical selection rule: choose the largest available source corpus rather than the most typologically similar language.

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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

Colleague,

Read this if you work on multilingual QA or transfer. The paper’s concrete contribution is a set of back-translation pipelines plus a Dual BERT model that jointly encodes machine-translated English and original Chinese instances, with a dynamic lambda weighting for the auxiliary source loss. The evaluation on CMRC 2018 and DRCD is real and realistic, unlike earlier work on tiny SQuAD translations, and the ablations show each component adds something. Code and datasets are available. That is the honest core, and it is a legitimate contribution.

The main soft spots are the usual for this kind of paper, plus one deeper one. The reported p-test is not specified: no test type, no number of runs, and no variance. The Japanese/French zero-shot comparison uses 304 samples per language, so those numbers are suggestive, not decisive. The 'state-of-the-art' claim is slightly overstated because several previous systems used different base models or training regimes; the paper acknowledges some are not directly comparable, but the abstract still says 'by a large margin.'

The deeper issue is the central mechanism. Section 4.2 says Dual BERT transfers span information through cross-lingual attention between multilingual BERT representations (Eq. 4), but no analysis shows those attention weights actually align Chinese and English token spans. Multilingual BERT is not reliably token-aligned for distant pairs, and the English side is a separate GNMT translation with different segmentation. Without such validation, the gains over the target-only baseline are equally consistent with the source branch acting as a second-view regularizer plus the auxiliary loss. That does not invalidate the empirical result, but it means the paper’s theoretical explanation is under-supported. The authors could check this with attention probes or by testing on a truly distant pair. I would not call it a fatal flaw; the empirical contribution stands on its own.

Who is this for? Researchers building MRC systems for low-resource languages and people working on cross-lingual transfer. It deserves serious peer review; the missing mechanism evidence should be requested in revision, not grounds for rejection.

Recommendation: engage; referee it, and ask for a proper significance test and at least one diagnostic of the bilingual attention.

Referee Report

4 major / 5 minor

Summary. The paper introduces Cross-Lingual Machine Reading Comprehension (CLMRC) as a task and proposes two families of approaches: zero-shot back-translation pipelines (GNMT, SimpleMatch, Answer Aligner, Answer Verifier) and a supervised model called Dual BERT that encodes target-language and machine-translated source-language inputs with multilingual BERT and combines them through a bilingual decoder with self-adaptive attention and dynamic auxiliary-loss weighting. Experiments are conducted on CMRC 2018, DRCD, and 304-sample Japanese and French SQuAD sets. The authors report state-of-the-art results on the Chinese datasets and claim that the proposed systems surpass human performance on some metrics.

Significance. If the reported gains are reliable, the paper offers a practical recipe for improving extractive MRC in low-resource languages by exploiting English SQuAD through machine translation and a shared multilingual encoder, and it would be one of the first systematic evaluations of such cross-lingual transfer on large realistic Chinese benchmarks. The paper's strengths include experiments on public Chinese datasets, a clear ablation study, comparisons within a controlled experimental setup, and a public code release. However, the central mechanism of Dual BERT is not directly validated, the significance testing is under-specified, and at least one headline claim is contradicted by the paper's own table; these issues need to be addressed before the claims are fully supported.

major comments (4)

[Abstract/§1 and Table 2] The Abstract and Section 1 claim that the proposed systems achieve state-of-the-art performance and 'even surpass human performance on some metrics.' Table 2 does not support this: on CMRC 2018 the best system reaches 73.6 EM/90.2 F1 on the test set while human performance is 92.4 EM/97.9 F1, and on DRCD the best system reaches 85.4 EM/91.6 F1 while human performance is 80.4 EM/93.3 F1 (the EM is lower but F1 is higher for the system, and no overall surpassing is visible). Please correct or explicitly qualify this overclaim.
[§5.2] The text states that all improvements over the baselines are 'verified by p-test with p < 0.05,' but it does not specify which statistical test was used, how many random seeds or runs were averaged, or whether significance was computed on the development, test, or challenge sets. Since the paper's central claim is of consistent and significant improvements, this reporting gap is load-bearing. Please provide the test name, the number of runs, and the exact procedure, or soften the significance claim accordingly.
[§4.2, Eq. (4) and §5.4] The bilingual decoder in Equation (4) computes softmax-filtered dot-product attention between target and source BERT token representations, and the ablation in Section 5.4 attributes the gain to cross-lingual transfer. However, no experiment verifies that this attention is actually aligned at the token level between Chinese and English, especially given that the source is produced by an independent GNMT translation with different segmentation and no alignment supervision. The observed gain is equally consistent with an auxiliary-training or regularization effect: the source branch is a second view of the same instance and the auxiliary loss adds an extra span-supervision signal. Please add a direct validation of the alignment mechanism, for example by measuring attention quality against word-level translation links, by shuffling the source token order, or by comparing against an auxiliary-loss-only baseline without cross-attention.
[§5.3 and Table 3] The Japanese and French SQuAD experiments use only 304 samples per language, and the improvement over Asai et al. (2018) is measured against a different base system (BERT-Large as the English reader versus their model). No significance test or confidence interval is reported for these differences. Please frame these zero-shot results accordingly, and either provide significance measures or explicitly describe the comparison as indicative rather than statistically verified.

minor comments (5)

[§5.1] The notation 'BERT Bcn' and 'BERT Bmul' in Table 2 is not defined in the text; please clarify which pre-trained checkpoints are used for the Chinese and multilingual variants.
[§4.2] In the sentence after Equation (4), the text refers to calculating the dot product between 'AST' and 'BS', while the equation uses Ã_TS; please make the notation consistent.
[§4.3] The phrase 'resulting in the start and target prediction PsS, PeS' appears to contain a typo; it should likely read 'start and end prediction.'
[§3.2] The relax parameter δ is introduced with a typical range but its exact value in the reported experiments is not specified; please state how δ was chosen.
[Figure 3] The curves in Figure 3 would be easier to interpret with error bars or an explicit statement of the number of random seeds used, since differences between small pre-training data sizes appear modest.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected; the proposed models are trained and evaluated on external datasets with no fitted parameter or self-citation that predetermines the claimed results.

full rationale

The paper's central claims are empirical improvements on public benchmarks (CMRC 2018, DRCD, Japanese/French SQuAD), and the derivation of Dual BERT does not reduce to its inputs by construction. The target span loss and auxiliary source span loss in Equations 9 and 13 are standard cross-entropy objectives, and the dynamic lambda in Equation 12 is computed per sample from cosine similarity between target and source representations rather than fitted to the test set. Self-citations to CMRC 2018 and prior HIT/SCIR work provide datasets or baselines, but the novel comparisons are against external state-of-the-art systems and independently reported results. The ablation study in Table 4 tests components such as the auxiliary loss, dynamic lambda, self-adaptive attention, and source BERT, and the observed degradations are consistent with the paper's claims without any architecturally forced equivalence. No equation or fitted value is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no known result is relabeled. Therefore the paper is self-contained against external benchmarks and shows no meaningful circularity.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No physical or conceptual entities are introduced; the paper’s contributions are model components (Dual BERT, Self-Adaptive Attention, dynamic lambda) that are the result itself, not postulates used to explain a result. The only hand-chosen parameter is the relax window delta, which does not affect the central claim.

free parameters (1)

relax parameter delta = range [0,5], not tuned
In Section 3.2, the sliding window length for SimpleMatch uses a relax parameter delta in [0,5]. The paper does not report the specific value used in experiments, so it is a hand-chosen range rather than a learned value.

assumptions (3)

domain assumption Multilingual BERT provides a shared cross-lingual representation space.
The Dual BERT decoder’s inter-attention (Eq. 1-5) assumes source and target BERT representations are comparable so that dot-product attention can transfer span information across languages.
domain assumption GNMT machine translation preserves enough answer-relevant information.
The back-translation approaches and Dual BERT rely on translated passages and questions retaining sufficient semantic fidelity for answer spans to transfer (Sections 3 and 4).
standard math Standard linear algebra and softmax operations are correct.
Equations 2-12 use matrix products and softmax without proof, which is standard from BERT and Transformer literature.

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Cite this review

Pith. "Pith review of Cross-Lingual Machine Reading Comprehension." pith.science (2026). https://pith.science/paper/BE7X5I2D

@misc{pith2026190900361,
  author       = {Pith},
  title        = {Pith review of: Cross-Lingual Machine Reading Comprehension},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BE7X5I2D}},
  note         = {Machine review of arXiv:1909.00361}
}

read the original abstract

Though the community has made great progress on Machine Reading Comprehension (MRC) task, most of the previous works are solving English-based MRC problems, and there are few efforts on other languages mainly due to the lack of large-scale training data. In this paper, we propose Cross-Lingual Machine Reading Comprehension (CLMRC) task for the languages other than English. Firstly, we present several back-translation approaches for CLMRC task, which is straightforward to adopt. However, to accurately align the answer into another language is difficult and could introduce additional noise. In this context, we propose a novel model called Dual BERT, which takes advantage of the large-scale training data provided by rich-resource language (such as English) and learn the semantic relations between the passage and question in a bilingual context, and then utilize the learned knowledge to improve reading comprehension performance of low-resource language. We conduct experiments on two Chinese machine reading comprehension datasets CMRC 2018 and DRCD. The results show consistent and significant improvements over various state-of-the-art systems by a large margin, which demonstrate the potentials in CLMRC task. Resources available: https://github.com/ymcui/Cross-Lingual-MRC

Figures

Figures reproduced from arXiv: 1909.00361 by the authors.

**Figure 2.** System overview of the Dual BERT model for cross-lingual machine reading comprehension task. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** BERT performance (average of EM and F1) with different amount of pre-training SQuAD (English) or DRCD (Traditional Chinese). thus the model is not that sensitive to the language in the pre-training stage. Also, as training data size of SQuAD is larger than DRCD, we could use more data for pre-training. When we add more SQuAD data (>25k) in the pre-training stage, the performance on the downstream task (CMRC 2018) … view at source ↗

Discussion (0). Continue with ORCID to comment.

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Works this paper leans on

23 extracted references · 8 canonical work pages

[1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

Mart \' n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning. In OSDI, volume 16, pages 265--283

work page 2016
[4]

Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2018. Multilingual extractive reading comprehension by runtime machine translation. arXiv preprint arXiv:1809.03275

work page Pith review arXiv 2018
[5]

Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu. 2018. http://aclweb.org/anthology/P18-1163 Towards robust neural machine translation . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1756--1766. Association for Computational Linguistics

work page 2018
[6]

Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2017. https://doi.org/10.18653/v1/P17-1055 Attention-over-attention neural networks for reading comprehension . In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 593--602. Association for Computational Linguistics

work page doi:10.18653/v1/p17-1055 2017
[7]

Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, and Guoping Hu. 2019. A span-extraction dataset for chinese machine reading comprehension. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing. Association for Comput...

work page 2019
[8]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long a...

doi:10.18653/v1/n19-1423 2019

Show all 23 references

[9]

Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. 2017. https://doi.org/10.18653/v1/P17-1168 Gated-attention readers for text comprehension . In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: ...

2017 doi
[10]

Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In Advances in Neural Information Processing Systems, pages 1684--1692

2015
[11]

Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015. The goldilocks principle: Reading children's books with explicit memory representations. arXiv preprint arXiv:1511.02301

2015 arXiv
[12]

Minghao Hu, Furu Wei, Yuxing Peng, Zhen Huang, Nan Yang, and Dongsheng Li. 2019. https://doi.org/10.1609/aaai.v33i01.33016529 Read + verify: Machine reading comprehension with unanswerable questions . Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):6529--6537

2019 doi
[13]

Rudolf Kadlec, Martin Schmid, Ond r ej Bajgar, and Jan Kleindienst. 2016. https://doi.org/10.18653/v1/P16-1086 Text understanding with the attention sum reader network . In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long ...

2016 doi
[14]

Diederik Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

2014 arXiv
[15]

Ting Liu, Yiming Cui, Qingyu Yin, Wei-Nan Zhang, Shijin Wang, and Guoping Hu. 2017. https://doi.org/10.18653/v1/P17-1010 Generating and exploiting large-scale pseudo training data for zero pronoun resolution . In Proceedings of the 55th Annual Meeting of the Association for Co...

2017 doi
[16]

Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. https://doi.org/10.18653/v1/D16-1264 Squad: 100,000+ questions for machine comprehension of text . In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383--23...

2016 doi
[17]

Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hananneh Hajishirzi. 2016. Bi-directional attention flow for machine comprehension. arXiv preprint arXiv:1611.01603

2016 arXiv
[18]

Chih Chieh Shao, Trois Liu, Yuting Lai, Yiying Tseng, and Sam Tsai. 2018. Drcd: a chinese machine reading comprehension dataset. arXiv preprint arXiv:1806.00920

2018 arXiv
[19]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998--6008

2017
[20]

Shuohang Wang and Jing Jiang. 2016. Machine comprehension using match-lstm and answer pointer. arXiv preprint arXiv:1608.07905

2016 arXiv
[21]

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144

2016 arXiv
[22]

Caiming Xiong, Victor Zhong, and Richard Socher. 2016. Dynamic coattention networks for question answering. arXiv preprint arXiv:1611.01604

2016 arXiv
[23]

Wei Yang, Yuqing Xie, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019. Data augmentation for bert fine-tuning in open-domain question answering. arXiv preprint arXiv:1904.06652

2019 arXiv

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

Mart \' n Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning. In OSDI, volume 16, pages 265--283

work page 2016

[4] [4]

Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, and Yoshimasa Tsuruoka. 2018. Multilingual extractive reading comprehension by runtime machine translation. arXiv preprint arXiv:1809.03275

work page Pith review arXiv 2018

[5] [5]

Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu. 2018. http://aclweb.org/anthology/P18-1163 Towards robust neural machine translation . In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1756--1766. Association for Computational Linguistics

work page 2018

[6] [6]

Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, and Guoping Hu. 2017. https://doi.org/10.18653/v1/P17-1055 Attention-over-attention neural networks for reading comprehension . In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 593--602. Association for Computational Linguistics

work page doi:10.18653/v1/p17-1055 2017

[7] [7]

Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, and Guoping Hu. 2019. A span-extraction dataset for chinese machine reading comprehension. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing. Association for Comput...

work page 2019

[8] [8]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long a...

doi:10.18653/v1/n19-1423 2019