Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14809))
Included in the following conference series:
509Accesses
Abstract
The task of Multiple Choice Question Answering (MCQA) aims to identify the correct answer from a set of candidates, given a background passage and an associated question. Considerable research efforts have been dedicated to addressing this task, leveraging a diversity of semantic matching techniques to estimate the alignment among the answer, passage, and question. However, key challenges arise as not all sentences from the passage contribute to the question answering, while only a few supporting sentences (clues) are useful. Existing clue extraction methods suffer from inefficiencies in identifying supporting sentences, relying on resource-intensive algorithms, pseudo labels, or overlooking the semantic coherence of the original passage. Addressing this gap, this paper introduces a novel extraction approach, termedConditionalClue extractor (ConClue), for MCQA. ConClue leverages the principles of Conditional Optimal Transport to effectively identify clues by transporting the semantic meaning of one or several words (from the original passage) to selected words (within identified clues), under the prior condition of the question and answer. Empirical studies on several competitive benchmarks consistently demonstrate the superiority of our proposed method over different traditional approaches, with a substantial average improvement of 1.1–2.5 absolute percentage points in answering accuracy.
This work is partially supported by the Australian Research Council Discovery Project (DP210101426), the Australian Research Council Linkage Project (LP200201035), AEGiS Advance Grant(888/008/268, University of Wollongong), and Telstra-UOW Hub for AIOT Solutions Seed Funding (2024).
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 8465
- Price includes VAT (Japan)
- Softcover Book
- JPY 10581
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Altschuler, J., Weed, J., Rigollet, P.: Near-linear time approximation algorithms for optimal transport via sinkhorn iteration. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1961–1971 (2017)
Berzak, Y., Malmaud, J., Levy, R.: STARC: structured annotations for reading comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 5726–5735 (2020)
Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, pp. 2292–2300 (2013)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: 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), pp. 4171–4186 (2019)
Huang, Z., Yu, P., Allan, J.: Improving cross-lingual information retrieval on low-resource languages via optimal transport distillation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, pp. 1048–1056 (2023)
Huang, Z., Wu, A., Shen, Y., Cheng, G., Qu, Y.: When retriever-reader meets scenario-based multiple-choice questions. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 985–994 (2021)
Huang, Z., Wu, A., Zhou, J., Gu, Y., Zhao, Y., Cheng, G.: Clues before answers: generation-enhanced multiple-choice QA. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 3272–3287 (2022)
Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E.: RACE: large-scale ReAding comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 785–794 (2017)
Li, R., Jiang, Z., Wang, L., Lu, X., Zhao, M., Chen, D.: Enhancing transformer-based language models with commonsense representations for knowledge-driven machine comprehension. Knowl.-Based Syst.220, 106936 (2021)
Luo, D., et al.: Evidence augment for multiple-choice machine reading comprehension by weak supervision. In: 30th International Conference on Artificial Neural Networks, pp. 357–368 (2021)
Malmaud, J., Levy, R., Berzak, Y.: Bridging information-seeking human gaze and machine reading comprehension. In: Proceedings of the 24th Conference on Computational Natural Language Learning, pp. 142–152 (2020)
Ni, J., Zhu, C., Chen, W., McAuley, J.: Learning to attend on essential terms: an enhanced retriever-reader model for open-domain question answering. In: 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), pp. 335–344 (2019)
Niu, Y., Jiao, F., Zhou, M., Yao, T., Xu, J., Huang, M.: A self-training method for machine reading comprehension with soft evidence extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3916–3927 (2020)
Nouri, N.: Text style transfer via optimal transport. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2532–2541 (2022)
Singh, J., McCann, B., Keskar, N.S., Xiong, C., Socher, R.: XLDA: cross-lingual data augmentation for natural language inference and question answering. In: The Eighth International Conference on Learning Representations (ICLR) (2020)
Sun, K., Yu, D., Chen, J., Yu, D., Choi, Y., Cardie, C.: Dream: a challenge data set and models for dialogue-based reading comprehension. Trans. Assoc. Comput. Linguist.7, 217–231 (2019)
Tabak, E.G., Trigila, G., Zhao, W.: Data driven conditional optimal transport. Mach. Learn.110(11), 3135–3155 (2021)
Villani, C., et al.: Optimal Transport: old and new, vol. 338 (2009)
Wei, Q., Ma, K., Liu, X., Ji, K., Yang, B., Abraham, A.: DIMN: dual integrated matching network for multi-choice reading comprehension. Eng. Appl. Artif. Intell.130, 107694 (2024)
Yao, X., et al.: Context-guided triple matching for multiple choice question answering. In: 2023 IEEE Smart World Congress (SWC), pp. 1–8. IEEE (2023)
Yao, X., Ma, J., Hu, X., Yang, J., Li, Y.F.: Improving machine reading comprehension through a simple masked-training scheme. In: Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pp. 222–232 (2023)
Yu, A.W., et al.: QANet: combining local convolution with global self-attention for reading comprehension. In: The Sixth International Conference on Learning Representations (ICLR) (2018)
Yu, H.T., Jatowt, A., Joho, H., Jose, J.M., Yang, X., Chen, L.: Wassrank: Listwise document ranking using optimal transport theory. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 24–32 (2019)
Zhang, S., Zhao, H., Wu, Y., Zhang, Z., Zhou, X., Zhou, X.: DCMN+: dual co-matching network for multi-choice reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 9563–9570 (2020)
Zhang, Z., Wu, Y., Zhou, J., Duan, S., Zhao, H., Wang, R.: SG-Net: syntax-guided machine reading comprehension. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 9636–9643 (2020)
Zhao, Y., Zhang, Z., Zhao, H.: Reference knowledgeable network for machine reading comprehension. IEEE/ACM Trans. Audio Speech Lang. Process.30, 1461–1473 (2022)
Zhu, P., Zhang, Z., Zhao, H., Li, X.: DUMA: reading comprehension with transposition thinking. IEEE/ACM Trans. Audio Speech Lang. Process.30, 269–279 (2021)
Author information
Authors and Affiliations
School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
Wangli Yang, Jie Yang & Wanqing Li
School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, Australia
Yi Guo
- Wangli Yang
You can also search for this author inPubMed Google Scholar
- Jie Yang
You can also search for this author inPubMed Google Scholar
- Wanqing Li
You can also search for this author inPubMed Google Scholar
- Yi Guo
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toJie Yang.
Editor information
Editors and Affiliations
Luleå Tekniska Universitet, Luleå, Sweden
Elisa H. Barney Smith
Luleå Tekniska Universitet, Luleå, Sweden
Marcus Liwicki
Tsinghua University, Beijing, China
Liangrui Peng
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, W., Yang, J., Li, W., Guo, Y. (2024). ConClue: Conditional Clue Extraction for Multiple Choice Question Answering. In: Barney Smith, E.H., Liwicki, M., Peng, L. (eds) Document Analysis and Recognition - ICDAR 2024. ICDAR 2024. Lecture Notes in Computer Science, vol 14809. Springer, Cham. https://doi.org/10.1007/978-3-031-70552-6_11
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-031-70551-9
Online ISBN:978-3-031-70552-6
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative