Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
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In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer.State-of-the-art models tend to use a pretrained transformer model (e.g., BERT) to encode the joint contextual information of document and question.However, these transformer-based models can only take a fixed-length (e.g., 512) text as its input.To deal with even longer text inputs, previous approaches usually chunk them into equally-spaced segments and predict answers based on each segment independently without considering the information from other segments.As a result, they may form segments that fail to cover the correct answer span or retain insufficient contexts around it, which significantly degrades the performance.Moreover, they are less capable of answering questions that need cross-segment information.We propose to let a model learn to chunk in a more flexible way via reinforcement learning: a model can decide the next segment that it wants to process in either direction.We also employ recurrent mechanisms to enable information to flow across segments.Experiments on three MRC datasets -CoQA, QuAC, and TriviaQA -demonstrate the effectiveness of our proposed recurrent chunking mechanisms: we can obtain segments that are more likely to contain complete answers and at the same time provide sufficient contexts around the ground truth answers for better predictions.
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Prior research has effectively leveraged context compression techniques such as retrieval and truncation to address long text tasks with pretrained language models.
“Prior research has effectively leveraged context compression techniques such as retrieval and truncation to address long text tasks with pretrained language models (Gong et al., 2020; Zhang et al., 2022; Zhao et al., 2022).”
From BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models (2023) · cites this paper as Gong et al., 2020
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 7,936 · see citing works
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · see citing works
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208 · see citing works
(1999) · Cited by 5,147 · see citing works
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,779 · see citing works
Know What You Don’t Know: Unanswerable Questions for SQuAD
Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/p18-2124 · Cited by 2,240 · see citing works
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Joshi, M., Choi, E., Weld, D., & Zettlemoyer, L. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1147 · Cited by 1,690 · see citing works
CoQA: A Conversational Question Answering Challenge
Reddy, S., Chen, D., & Manning, C. D. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00266 · Cited by 975 · see citing works
QuAC: Question Answering in Context
Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1241 · Cited by 714 · see citing works
NewsQA: A Machine Comprehension Dataset
Trischler, A., Wang, T., Yuan, X., Harris, J., Sordoni, A., Bachman, P., & Suleman, K. (2017) · Proceedings of the 2nd Workshop on Representation Learning for NLP · doi:10.18653/v1/w17-2623 · Cited by 706 · see citing works
The NarrativeQA Reading Comprehension Challenge
Kočiský, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., & Grefenstette, E. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00023 · Cited by 596 · see citing works
The Goldilocks Principle: Reading Children's Books with Explicit Memory RepresentationsPreprint
Hill, F., Bordes, A., Chopra, S., & Weston, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02301 · Cited by 478 · see citing works
End-to-End Open-Domain Question Answering with BERTserini
Yang, W., Xie, Y., Lin, A., Li, X., Tan, L., Xiong, K., Li, M., & Lin, J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-4013 · Cited by 472 · see citing works
Simple and Effective Multi-Paragraph Reading Comprehension
Clark, C., & Gardner, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1078 · Cited by 402 · see citing works
Neural Legal Judgment Prediction in English
Chalkidis, I., Androutsopoulos, I., & Aletras, N. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1424 · Cited by 352 · see citing works
ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading ComprehensionPreprint
Zhang, S., Liu, X., Liu, J., Gao, J., Duh, K., & Durme, B. V. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.12885 · Cited by 201 · see citing works
Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Hu, M., Wei, F., Peng, Y., Huang, Z., Yang, N., & Zhou, M. (2018) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016529 · Cited by 166 · see citing works
Automatically learning construction injury precursors from text
Baker, H., Hallowell, M. R., & Tixier, A. J. ‐. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103145 · Cited by 149 · see citing works
Efficient and Robust Question Answering from Minimal Context over Documents
Min, S., Zhong, V., Socher, R., & Xiong, C. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1160 · Cited by 147 · see citing works
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Lin, Y., Ji, H., Liu, Z., & Sun, M. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-1161 · Cited by 143 · see citing works
Interpretation of Natural Language Rules in Conversational Machine Reading
Saeidi, M., Bartolo, M., Lewis, P., Singh, S., Rocktäschel, T., Sheldon, M., Bouchard, G., & Riedel, S. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1233 · Cited by 141 · see citing works
Yu, A. W., Lee, H., & Le, Q. V. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1172 · Cited by 96 · see citing works
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Gong, H., Bhat, S., Wu, L., Xiong, J., & Hwu, W. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1320 · Cited by 80 · see citing works
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Seo, M., Min, S., Farhadi, A., & Hajishirzi, H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02085 · Cited by 69 · see citing works
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Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-T., Choi, Y., Liang, P., & Zettlemoyer, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07036 · Cited by 58 · see citing works
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
Yatskar, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1241 · Cited by 56 · see citing works
Training a Ranking Function for Open-Domain Question Answering
Htut, P. M., Bowman, S. R., & Cho, K. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/n18-4017 · Cited by 45 · see citing works
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuACPreprint
Yatskar, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10735 · Cited by 31 · see citing works
Accurate Supervised and Semi-Supervised Machine Reading for Long Documents
Hewlett, D., Jones, L., Lacoste, A., & Gür, İ. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1214 · Cited by 27 · see citing works
Unifying Question Answering and Text Classification via Span Extraction.Preprint
Keskar, N. S., McCann, B., Xiong, C., & Socher, R. (2019) · arXiv (Cornell University) · Cited by 20 · see citing works
Review Conversational Reading ComprehensionPreprint
Xu, H., Liu, B., Shu, L., & Yu, P. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00821 · Cited by 17 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
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- Authors
- Gong, H., Shen, Y., Yu, D., Chen, J., & Yu, D.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Microsoft (United States) (US)
- Corresponding author
- Gong, Hongyu
- Venue
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Published
- 2020
- Pages
- 6751-6761
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W3035391452
- Cited by
- 42
- References
- 35
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 12 · 2022: 8 · 2021: 7 · 2020: 3
- FWCI
- 2.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- chunking, reinforcement learning, segment selection, QuAC, TriviaQA
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