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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Multi-turn Inference Matching Network for Natural Language Inference

verfasst von : Chunhua Liu, Shan Jiang, Hainan Yu, Dong Yu

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Natural Language Inference (NLI) is a fundamental and challenging task in Natural Language Processing (NLP). Most existing methods only apply one-pass inference process on a mixed matching feature, which is a concatenation of different matching features between a premise and a hypothesis. In this paper, we propose a new model called Multi-turn Inference Matching Network (MIMN) to perform multi-turn inference on different matching features. In each turn, the model focuses on one particular matching feature instead of the mixed matching feature. To enhance the interaction between different matching features, a memory component is employed to store the history inference information. The inference of each turn is performed on the current matching feature and the memory. We conduct experiments on three different NLI datasets. The experimental results show that our model outperforms or achieves the state-of-the-art performance on all the three datasets.

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Vorheriges Kapitel Chinese Grammatical Error Correction Using Statistical and Neural Models
Nächstes Kapitel From Humour to Hatred: A Computational Analysis of Off-Colour Humour
Literatur
1.
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning (2016)
2.
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR,abs/1409.0473, September 2014
3.
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: EMNLP (2015)
4.
Chen, Q., Zhu, X.D., Ling, Z.H., Wei, S., Jiang, H., Inkpen, D.: Recurrent neural network-based sentence encoder with gated attention for natural language inference. In: EMNLP (2017)
5.
Chen, Q., Zhu, X., Ling, Z.H., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: ACL, Vancouver, pp. 1657–1668, July 2017
6.
Cheng, J., Dong, L., Lapata, M.: Long short-term memory-networks for machine reading. In: EMNLP, Austin, pp. 551–561, November 2016
7.
Dagan, I., Glickman, O., Magnini, B.: The PASCAL recognising textual entailment challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006). https://​doi.​org/​10.​1007/​11736790_​9CrossRef
8.
Ghaeini, R., et al.: DR-BiLSTM: dependent reading bidirectional LSTM for natural language inference. arXiv preprint arXiv:​1802.​05577 (2018)
9.
Glickman, O., Dagan, I.: A probabilistic setting and lexical cooccurrence model for textual entailment. In: Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, EMSEE 2005 (2005)
10.
Gong, Y., Luo, H., Zhang, J.: Natural language inference over interaction space. In: ICLR (2018)
11.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRef
12.
Khot, T., Sabharwal, A., Clark, P.: SciTail: a textual entailment dataset from science question answering. In: AAAI (2018)
13.
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR,abs/1412.6980 (2014)
14.
Lai, A., Bisk, Y., Hockenmaier, J.: Natural language inference from multiple premises. In: IJCNLP, pp. 100–109 (2017)
15.
Liu, P., Qiu, X., Chen, J., Huang, X.: Deep fusion LSTMs for text semantic matching. In: ACL, pp. 1034–1043 (2016)
16.
Liu, P., Qiu, X., Zhou, Y., Chen, J., Huang, X.: Modelling interaction of sentence pair with coupled-LSTMs. In: EMNLP (2016)
17.
Liu, Y., Sun, C., Lin, L., Wang, X.: Learning natural language inference using bidirectional LSTM model and inner-attention. CoRR,abs/1605.09090 (2016)
18.
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: EMNLP, pp. 1412–1421 (2015)
19.
MacCartney, B., Galley, M., Manning, C.D.: A phrase-based alignment model for natural language inference. In: EMNLP 2008 (2008)
20.
Marelli, M., Menini, S., Baroni, M., Bentivogli, L., bernardi, R., Zamparelli, R.: A sick cure for the evaluation of compositional distributional semantic models. In: LREC (2014)
21.
McCann, B., Bradbury, J., Xiong, C., Socher, R.: Learned in translation: contextualized word vectors. arXiv preprint arXiv:​1708.​00107 (2017)
22.
Mou, L., et al.: Natural language inference by tree-based convolution and heuristic matching. In: ACL (2016)
23.
Munkhdalai, T., Yu, H.: Neural semantic encoders. In: EACL, pp. 397–407 (2016)
24.
Nie, Y., Bansal, M.: Shortcut-stacked sentence encoders for multi-domain inference. In: RepEval@EMNLP (2017)
25.
Parikh, A., Täckström, O., Das, D., Uszkoreit, J.: A decomposable attention model for natural language inference. In: EMNLP, Austin, pp. 2249–2255, November 2016
26.
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP (2014)
27.
Rocktäschel, T., Grefenstette, E., Hermann, K.M., Kocisky, T., Blunsom, P.: Reasoning about entailment with neural attention. In: ICLR (2016)
28.
Sha, L., Chang, B., Sui, Z., Li, S.: Reading and thinking: re-read LSTM unit for textual entailment recognition. In: COLING, pp. 2870–2879, December 2016
29.
Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., Zhang, C.: DiSAN: directional self-attention network for RNN/CNN-free language understanding. CoRR,abs/1709.04696 (2017)
30.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetMATH
31.
Tai, K.S., Socher, R., Manning, C.D.: Improved semantic representations from tree-structured long short-term memory networks. In: ACL, Beijing, pp. 1556–1566, July 2015
32.
Tay, Y., Tuan, L.A., Hui, S.C.: A compare-propagate architecture with alignment factorization for natural language inference. arXiv preprint arXiv:​1801.​00102 (2017)
33.
Wang, S., Jiang, J.: A compare-aggregate model for matching text sequences. CoRR,abs/1611.01747 (2016)
34.
Wang, S., Jiang, J.: Learning natural language inference with LSTM. In: NAACL, San Diego, pp. 1442–1451, June 2016
35.
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)
36.
Wang, Z., Wael, H., Radu, F.: Bilateral multi-perspective matching for natural language sentences. In: IJCAI, pp. 4144–4150 (2017)
37.
Weston, J., Chopra, S., Bordes, A.: Memory networks. In: ICLR (2015)
38.
Yu, H., Munkhdalai, T.: Neural tree indexers for text understanding. In: ACL, vol. 1, pp. 11–21 (2017)
Metadaten
Titel
Multi-turn Inference Matching Network for Natural Language Inference
verfasst von
Chunhua Liu
Shan Jiang
Hainan Yu
Dong Yu
Copyright-Jahr
2018
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-99500-7

Electronic ISBN: 978-3-319-99501-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-99501-4_11

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