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Row-based hierarchical graph network for multi-hop question answering over textual and tabular data

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Abstract

Multi-hop Question Answering over heterogeneous data is a challenging task in Natural Language Processing(NLP), which aims to find the answer among heterogeneous data sources and reasoning chains. When facing complex reasoning scenarios, most existing QA systems can only focus on some specific types of data. To solve this issue, we propose a new approach based on Row Hierarchical Graph Network(RHGN), which can accomplish multi-hop QA over both textual and tabular data. Specifically, RHGN consists of two phases: the row selection phase is designed to find the table row that most likely contains the answer, and the row reading comprehension phase that aims to locate the final answer in the answer row. In the row selection phase, we utilize a retriever to search all the supporting evidence related to the question, and a pre-training language model is employed to select the appropriate answer row. In the succeeding stage of row reading comprehension, we propose a row-based hierarchical graph network to capture the structural information, and a gated mechanism is used to perform graph reasoning. Eventually, the optimum final answer can be obtained by three interrelated sub-tasks. The experimental results demonstrate the effectiveness of RHGN and it achieves superior performance on the HybridQA dataset.

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Some data, models and code generated or used during the study will be available under reasonable request from the corresponding author.

References

  1. Devlin J, Chang M-W, Lee K, Toutanova K (2019) 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, vol 1. Association for Computational Linguistics, Minneapolis, Minnesota, pp 4171–4186. https://doi.org/10.18653/v1/N19-1423. https://www.aclweb.org/anthology/N19-1423

  2. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692

  3. Yang Z, Qi P, Zhang S, Bengio Y, Cohen W, Salakhutdinov R, Manning CD (2018) HotpotQA: a dataset for diverse, explainable multi-hop question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Brussels, Belgium, pp 2369–2380. https://doi.org/10.18653/v1/D18-1259. https://www.aclweb.org/anthology/D18-1259

  4. Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Austin, Texas, pp 2383–2392. https://doi.org/10.18653/v1/D16-1264. https://www.aclweb.org/anthology/D16-1264

  5. Pasupat P, Liang P (2015) Compositional semantic parsing on semi-structured tables. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, vol 1 Long Papers. The Association for Computer Linguistics, Beijing, China, pp 1470–1480. https://doi.org/10.3115/v1/p15-1142. https://doi.org/10.3115/v1/p15-1142

  6. Berant J, Chou A, Frostig R, Liang P (2013) Semantic parsing on Freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Seattle, Washington, USA, pp 1533–1544. https://www.aclweb.org/anthology/D13-1160

  7. Talmor A, Berant J (2018) The web as a knowledge-base for answering complex questions. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol 1 (Long Papers), Association for Computational Linguistics, New Orleans, Louisiana, pp 641–651. https://doi.org/10.18653/v1/N18-1059. https://www.aclweb.org/anthology/N18-1059

  8. Min S, Zhong V, Zettlemoyer L, Hajishirzi H (2019) Multi-hop reading comprehension through question decomposition and rescoring. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, pp 6097–6109. https://doi.org/10.18653/v1/P19-1613. https://www.aclweb.org/anthology/P19-1613

  9. Yadav V, Bethard S, Surdeanu M (2020) Unsupervised alignment-based iterative evidence retrieval for multi-hop question answering. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, pp 4514–4525. https://doi.org/10.18653/v1/2020.acl-main.414. https://www.aclweb.org/anthology/2020.acl-main.414

  10. Asai A, Hashimoto K, Hajishirzi H, Socher R, Xiong C (2020) Learning to retrieve reasoning paths over wikipedia graph for question answering. In: International Conference on Learning Representations. https://openreview.net/forum?id=SJgVHkrYDH

  11. Zhong W, Tang D, Feng Z, Duan N, Zhou M, Gong M, Shou L, Jiang D, Wang J, Yin J (2020) LogicalFactChecker: leveraging logical operations for fact checking with graph module network. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 6053–6065. https://doi.org/10.18653/v1/2020.acl-main.539. https://www.aclweb.org/anthology/2020.acl-main.539

  12. Yu T, Wu C, Lin XV, Wang B, Tan YC, Yang X, Radev DR, Socher R, Xiong C (2020) Grappa: grammar-augmented pre-training for table semantic parsing. CoRR abs/2009.13845arXiv:2009.13845

  13. Yin P, Neubig G, Yih W-t, Riedel S (2020) TaBERT: pretraining for joint understanding of textual and tabular data. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, pp 8413–8426. https://doi.org/10.18653/v1/2020.acl-main.745. https://www.aclweb.org/anthology/2020.acl-main.745

  14. Sun H, Bedrax-Weiss T, Cohen WW (2019) Pullnet: open domain question answering with iterative retrieval on knowledge bases and text. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Association for Computational Linguistics, Hong Kong, China, pp 2380–2390. https://doi.org/10.18653/v1/D19-1242. https://doi.org/10.18653/v1/D19-1242

  15. Chen W, Zha H, Chen Z, Xiong W, Wang H, Wang WY (2020) HybridQA: a dataset of multi-hop question answering over tabular and textual data. In: Findings of the Association for Computational Linguistics: EMNLP 2020, Association for Computational Linguistics, Online, pp 1026–1036. https://doi.org/10.18653/v1/2020.findings-emnlp.91. https://www.aclweb.org/anthology/2020.findings-emnlp.91

  16. Zhu F, Lei W, Huang Y, Wang C, Zhang S, Lv J, Feng F, Chua T (2021) TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance. In: Zong, C., Xia, F., Li, W., Navigli, R. (eds.) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol 1. Association for Computational Linguistics, Online, pp 3277–3287. https://doi.org/10.18653/v1/2021.acl-long.254. https://doi.org/10.18653/v1/2021.acl-long.254

  17. Chen W, Chang M, Schlinger E, Wang WY, Cohen WW (2021) Open question answering over tables and text. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria. OpenReview.net, Austria. https://openreview.net/forum?id=MmCRswl1UYl

  18. Sun H, Cohen WW, Salakhutdinov R (2021) End-to-end multi-hop retrieval for compositional question answering over long documents

  19. De Cao N, Aziz W, Titov I (2019) Question answering by reasoning across documents with graph convolutional networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol 1. Association for Computational Linguistics, Minneapolis, Minnesota, pp 2306–2317. https://doi.org/10.18653/v1/N19-1240. https://www.aclweb.org/anthology/N19-1240

  20. Tu M, Wang G, Huang J, Tang Y, He X, Zhou B (2019) Multi-hop reading comprehension across multiple documents by reasoning over heterogeneous graphs. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, pp 2704–2713. https://doi.org/10.18653/v1/P19-1260. https://www.aclweb.org/anthology/P19-1260

  21. Fang Y, Sun S, Gan Z, Pillai R, Wang S, Liu J (2020) Hierarchical graph network for multi-hop question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online, pp 8823–8838. https://doi.org/10.18653/v1/2020.emnlp-main.710. https://www.aclweb.org/anthology/2020.emnlp-main.710

  22. Tu M, Huang K, Wang G, Huang J, Zhou B (2020) Select, answer and explain: interpretable multi-hop reading comprehension over multiple documents. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34. pp 9073–9080

  23. Herzig J, Nowak PK, Müller T, Piccinno F, Eisenschlos J (2020) TaPas: weakly supervised table parsing via pre-training. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Online, pp 4320–4333. https://doi.org/10.18653/v1/2020.acl-main.398. https://www.aclweb.org/anthology/2020.acl-main.398

  24. Wang Y, Bao J, Duan C, Wu Y, He X, Zhao T (2022) MuGER\(^2\): multi-granularity evidence retrieval and reasoning for hybrid question answering. arXiv. https://doi.org/10.48550/ARXIV.2210.10350. arXiv:https://arxiv.org/abs/2210.10350

  25. Kumar V, Chemmengath S, Gupta Y, Sen J, Bharadwaj S, Chakrabarti S (2021) Multi-instance training for question answering across table and linked text

  26. Feng Y, Han Z, Sun M, Li P (2022) Multi-hop open-domain question answering over structured and unstructured knowledge. In: Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics, Seattle, United States, pp 151–156. https://doi.org/10.18653/v1/2022.findings-naacl.12. https://aclanthology.org/2022.findings-naacl.12

  27. Hwang W, Yim J, Park S, Seo M (2019) A comprehensive exploration on wikisql with table-aware word contextualization. arXiv preprint arXiv:1902.01069

  28. Joshi M, Levy O, Zettlemoyer L, Weld D (2019) BERT for coreference resolution: baselines and analysis. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, pp 5803–5808. https://doi.org/10.18653/v1/D19-1588. https://www.aclweb.org/anthology/D19-1588

  29. Beltagy I, Peters ME, Cohan A (2020) Longformer: the long-document transformer

  30. Yu T, Zhang R, Yang K, Yasunaga M, Wang D, Li Z, Ma J, Li I, Yao Q, Roman S, Zhang Z, Radev DR (2018) Spider: a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, Association for Computational Linguistics, Belgium, pp 3911–3921. https://doi.org/10.18653/v1/d18-1425. https://doi.org/10.18653/v1/d18-1425

  31. Parikh AP, Wang X, Gehrmann S, Faruqui M, Dhingra B, Yang D, Das D (2020) Totto: a controlled table-to-text generation dataset. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online, pp 1173–1186. https://doi.org/10.18653/v1/2020.emnlp-main.89. https://doi.org/10.18653/v1/2020.emnlp-main.89

  32. Eisenschlos J, Gor M, Müller T, Cohen W (2021) MATE: multi-view attention for table transformer efficiency. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, pp 7606–7619. https://doi.org/10.18653/v1/2021.emnlp-main.600. https://aclanthology.org/2021.emnlp-main.600

  33. Zhang H, Wang Y, Wang S, Cao X, Zhang F, Wang Z (2020) Table fact verification with structure-aware transformer. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online, pp 1624–1629. https://doi.org/10.18653/v1/2020.emnlp-main.126. https://aclanthology.org/2020.emnlp-main.126

  34. Wang S, Li BZ, Khabsa M, Fang H, Ma H (2020) Linformer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768

  35. Joshi M, Chen D, Liu Y, Weld D, Zettlemoyer L, Levy O (2020) Spanbert: improving pre-training by representing and predicting spans. Trans Assoc Comput Linguist 8:64–77

    Article  Google Scholar 

  36. Lyu C, Shang L, Graham Y, Foster J, Jiang X, Liu Q (2021) Improving unsupervised question answering via summarization-informed question generation. In: Moens, M., Huang, X., Specia, L., Yih, S.W. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, Association for Computational Linguistics, Online, pp 4134–4148. https://doi.org/10.18653/v1/2021.emnlp-main.340. https://doi.org/10.18653/v1/2021.emnlp-main.340

  37. Pan L, Chen W, Xiong W, Kan M-Y, Wang WY (2021) Unsupervised multi-hop question answering by question generation. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics, Online, pp 5866–5880. https://doi.org/10.18653/v1/2021.naacl-main.469. https://aclanthology.org/2021.naacl-main.469

  38. Shakeri S, Nogueira dos Santos C, Zhu H, Ng P, Nan F, Wang Z, Nallapati R, Xiang B (2020) End-to-end synthetic data generation for domain adaptation of question answering systems. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online, pp 5445–5460. https://doi.org/10.18653/v1/2020.emnlp-main.439. https://aclanthology.org/2020.emnlp-main.439

  39. Sun N, Yang X, Liu Y (2020) Tableqa: a large-scale chinese text-to-sql dataset for table-aware SQL generation. CoRR abs/2006.06434arXiv:2006.06434

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62272100, and in part by the Fundamental Research Funds for the Central Universities and the Academy-Locality Cooperation Project of Chinese Academy of Engineering under Grant JS2021ZT05.

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    1. Key Laboratory of Computer Network and Information Integration (Southeast University, Ministry of Education), School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China

      Peng Yang, Wenjun Li, Guangzhen Zhao & Xianyu Zha

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    1. Wenjun Li have contributed equally to this work.

      • Wenjun Li
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    Yang, P., Li, W., Zhao, G. et al. Row-based hierarchical graph network for multi-hop question answering over textual and tabular data. J Supercomput 79, 9795–9818 (2023). https://doi.org/10.1007/s11227-022-05035-9

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