ConClue: Conditional Clue Extraction for Multiple Choice Question Answering
Authors: 
Wangli Yang, Jie Yang, Wanqing Li, Yi GuoAuthors Info & Claims
Wangli Yang, Jie Yang, Wanqing Li, Yi GuoAuthors Info & ClaimsPages 183 - 198
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, termed Conditional Clue 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.
References
[1]
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)
[2]
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)
[3]
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)
[4]
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)
[5]
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)
[6]
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)
[7]
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)
[8]
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)
[9]
Li R, Jiang Z, Wang L, Lu X, Zhao M, and Chen D Enhancing transformer-based language models with commonsense representations for knowledge-driven machine comprehension Knowl.-Based Syst. 2021 220
[10]
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)
[11]
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)
[12]
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)
[13]
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)
[14]
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)
[15]
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)
[16]
Sun K, Yu D, Chen J, Yu D, Choi Y, and Cardie C Dream: a challenge data set and models for dialogue-based reading comprehension Trans. Assoc. Comput. Linguist. 2019 7 217-231
[17]
Tabak EG, Trigila G, and Zhao W Data driven conditional optimal transport Mach. Learn. 2021 110 11 3135-3155
[18]
Villani, C., et al.: Optimal Transport: old and new, vol. 338 (2009)
[19]
Wei Q, Ma K, Liu X, Ji K, Yang B, and Abraham A DIMN: dual integrated matching network for multi-choice reading comprehension Eng. Appl. Artif. Intell. 2024 130
[20]
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)
[21]
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)
[22]
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)
[23]
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)
[24]
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)
[25]
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)
[26]
Zhao Y, Zhang Z, and Zhao H Reference knowledgeable network for machine reading comprehension IEEE/ACM Trans. Audio Speech Lang. Process. 2022 30 1461-1473
[27]
Zhu P, Zhang Z, Zhao H, and Li X DUMA: reading comprehension with transposition thinking IEEE/ACM Trans. Audio Speech Lang. Process. 2021 30 269-279
Index Terms
- ConClue: Conditional Clue Extraction for Multiple Choice Question Answering
Index terms have been assigned to the content through auto-classification.
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Information & Contributors
Information
Published In
Aug 2024
455 pages
ISBN:978-3-031-70551-9
DOI:10.1007/978-3-031-70552-6
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 September 2024
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