Abstract
Text-based question-answering (QA in short) is a popular application on multimedia environments. In this paper, we mainly focus on the multi-paragraphs QA systems, which can retrieve many candidate paragraphs to feed into the extraction module to locate the answers in the paragraphs. However, according to our observations, there are no real answer in many candidate paragraphs. To filter these paragraphs, we propose a multi-level fused sequence matching (MFM in short) model through deep network methods. Then we construct a distant supervision dataset based on Wikipedia and carry out several experiments on that. Also we use another popular sequence matching dataset to test the performance of our model. Experiments show that our MFM model can outperform recent models not only on the filtering candidates in multi-paragraphs QA task but also on the sequence matching task.
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References
Chen, D., Fisch, A., Weston, J., et al.: Reading wikipedia to answer open-domain questions, 1870–1879 (2017)
Wang, S., Jiang, J.: A compare-aggregate model for matching text sequences. In: Conference on ICLR 2017 (2017)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Huang, H.Y., Zhu, C., Shen, Y., et al.: FusionNet: fusing via fully-aware attention with application to machine comprehension (2017)
Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends\(\textregistered \) Inf. Retr. 3(4), 333–389 (2009)
Yin, W., Schütze, H., Xiang, B., et al.: ABCNN: attention-based convolutional neural network for modeling sentence Pairs. Comput. Sci. (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. In: Supervised Sequence Labelling with Recurrent Neural Networks, pp. 1735–1780. Springer, Heidelberg (1997)
Zaragoza, H., Craswell, N., Taylor, M.J., et al.: Microsoft Cambridge at TREC 13: web and hard tracks. In: TREC 2004 (2004)
Xiong, C., Zhong, V., Socher, R.: Dynamic coattention networks for question answering (2016)
Wang, Z., Hamza, W., Florian, R.: Bilateral multi-perspective matching for natural language sentences (2017)
Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: Research and Development in Information Retrieval, pp. 275–281 (1998)
Kadlec, R., Schmid, M., Bajgar, O., et al.: Text understanding with the attention sum reader network, 908–918 (2016)
Seo, M., Kembhavi, A., Farhadi, A., et al.: Bidirectional attention flow for machine comprehension (2016)
Tan, M., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection. Comput. Sci. (2015)
He, H., Lin, J.: Pairwise word interaction modeling with deep neural networks for semantic similarity measurement. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 937–948 (2016)
Yu, L., Hermann, K.M., Blunsom, P., et al.: Deep learning for answer sentence selection. Comput. Sci. (2014)
Bowman, S.R., Angeli, G., Potts, C., et al.: A large annotated corpus for learning natural language inference. Comput. Sci. (2015)
Feng, M., Xiang, B., Glass, M.R., et al.: Applying deep learning to answer selection: a study and an open task, 813–820 (2015)
Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading (2016)
Wang, S., Jiang, J.: Learning natural language inference with LSTM (2015)
Rocktaschel, T., Grefenstette, E., Hermann, K.M., et al.: Reasoning about entailment with neural attention (2015)
Tan, M., Santos, C.D., Xiang, B., et al.: Improved representation learning for question answer matching. In: Meeting of the Association for Computational Linguistics, pp. 464–473 (2016)
Hermann, K.M., Kociský, T., Grefenstette, E., et al.: Teaching machines to read and comprehend, 1693–1701 (2015)
Chen, Q., Zhu, X., Ling, Z., et al.: Enhancing and combining sequential and tree LSTM for natural language inference, 1657–1668 (2016)
Parikh, A.P., Täckström, O., Das, D., et al.: A decomposable attention model for natural language inference, 2249–2255 (2016)
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Liu, Y. et al. (2018). MFM: A Multi-level Fused Sequence Matching Model for Candidates Filtering in Multi-paragraphs Question-Answering. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_41
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DOI: https://doi.org/10.1007/978-3-030-00764-5_41
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