Abstract
Multi-hop reasoning is an essential part of the current reading comprehension and question answering areas. The reasoning methods have been extensively studied, and most of them are generally focused on the pre-retrieval based inference, with the help of a few paragraphs. These methods are fixed and unable to cope with dynamic and complex questions. Here, we propose to utilize the dynamic graph reasoning network for multi-hop reading comprehension question answering.
Specifically, the new approach continuously infers the clue entities and candidate answers based on the question and clue paragraphs. The clue entities and candidate answers extracted at each hop are used as new nodes to expand the dynamic graph. Then we iteratively update the semantic representation of the questions via dynamic question memory, and apply the graph attention network to encode the information of inference paths. Extensive experiments on two datasets verify the advantage and improvements of the proposed approach.
This work was supported by NSFC grant 61972151, the Open Research Fund of KLATASDS-MOE.
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Cao, N.D., Aziz, W., Titov, I.: Question answering by reasoning across documents with graph convolutional networks. In: NAACL, pp. 2306–2317 (2019)
Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: ACL, pp. 1870–1879 (2017)
Das, R., Dhuliawala, S., Zaheer, M., McCallum, A.: Multi-step retriever-reader interaction for scalable open-domain question answering. In: ICLR (2019)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, pp. 4171–4186 (2019)
Ding, M., Zhou, C., Chen, Q., Yang, H., Tang, J.: Cognitive graph for multi-hop reading comprehension at scale. ACL 2019, 2694–2703 (2019)
Feldman, Y., El-Yaniv, R.: Multi-hop paragraph retrieval for open-domain question answering. In: ACL, pp. 2296–2309, July 2019
Kumar, A., et al.: Ask me anything: dynamic memory networks for natural language processing. In: ICML, pp. 1378–1387 (2016)
Lee, K., Chang, M., Toutanova, K.: Latent retrieval for weakly supervised open domain question answering. In: ACL, pp. 6086–6096 (2019)
Lin, B.Y., Chen, X., Chen, J., Ren, X.: KagNet: knowledge-aware graph networks for commonsense reasoning. In: Proceedigs of EMNLP-IJCNLP (2019)
Rajani, N.F., McCann, B., Xiong, C., Socher, R.: Explain yourself! Leveraging language models for commonsense reasoning. In: ACL (2019)
Seo, M.J., Kembhavi, A., Farhadi, A., Hajishirzi, H.: Bidirectional attention flow for machine comprehension. In: ICLR (2017)
Talmor, A., Herzig, J., Lourie, N., Berant, J.: CommonsenseQA: a question answering challenge targeting commonsense knowledge. In: NAACL (2019)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: ICLR, pp. 1–12 (2018)
Wang, W., Yang, N., Wei, F., Chang, B., Zhou, M.: Gated self-matching networks for reading comprehension and question answering. In: ACL, pp. 189–198 (2017)
Xiao, Y., et al.: Dynamically fused graph network for multi-hop reasoning (2019). arxiv:1905.06933Comment. Accepted by ACL 19
Yang, Z., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering. In: EMNLP, pp. 2369–2380 (2018)
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Xu, L., Yao, J., Zhang, Y. (2020). Dynamic Multi-hop Reasoning. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_39
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DOI: https://doi.org/10.1007/978-3-030-60259-8_39
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