Abstract
Natural language inference (NLI), earlier known as textual entailment, is an important task related to the semantic matching of natural language sentences. Systems and methodologies that can identify inferences are helpful for most language processing tasks like document summarization and question answering systems. There is active research in NLI for English and other foreign languages. Considering Indian languages like Malayalam, there are very few works done. Here, we focus on identifying inferences in the Malayalam language using one-dimensional convolutional neural network (CNN), multichannel CNN, and CNN architecture on matching sentences over interaction space with fastText-based embeddings. This work is an attempt to apply convolutional neural networks for NLI in Malayalam without any hand-engineered features. This approach contributes with a recall of 0.66% for binary and 0.51% for multiclass classification. This work also contributes to the language resources community.
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References
Anand Kumar, M., Singh, S., Kavirajan, B., Soman, K.: DPIL@FIRE 2016: overview of shared task on detecting paraphrases in Indian languages (DPIL). In: CEUR Workshop Proceedings, vol. 1737, pp. 233–238 (2016)
Artetxe, M., Schwenk, H.: Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Trans. Assoc. Comput. Linguist. 7, 597–610 (2019)
Bos, J., Zanzotto, F.M., Pennacchiotti, M.: Textual entailment at EVALITA 2009. Proc. EVALITA 2009(6.4), 2 (2009)
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326 (2015)
Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A.: Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364 (2017)
Conneau, A., Rinott, R., Lample, G., Williams, A., Bowman, S.R., Schwenk, H., Stoyanov, V.: XNLI: evaluating cross-lingual sentence representations. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2018)
Dagan, I., Glickman, O., Magnini, B.: The pascal recognising textual entailment challenge. In: Machine Learning Challenges Workshop, pp. 177–190. Springer (2005)
Das, A., Pal, D.R.: Exploring the partial textual entailment problem for Bengali news texts. Res. Comput. Sci. 86, 43–52 (2014)
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018). http://arxiv.org/abs/1810.04805
Ghuge, S., Bhattacharya, A.: Survey in textual entailment (2014)
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)
Guo, M., Zhang, Y., Zhao, D., Liu, T.: Generating textual entailment using residual LSTMs. In: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, pp. 263–272. Springer (2017)
Hu, B., Lu, Z., Li, H., Chen, Q.: Convolutional neural network architectures for matching natural language sentences. In: Advances in Neural Information Processing Systems, pp. 2042–2050 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Bernardi, R., Zamparelli, R., et al.: A sick cure for the evaluation of compositional distributional semantic models. In: LREC, pp. 216–223 (2014)
Mou, L., Men, R., Li, G., Xu, Y., Zhang, L., Yan, R., Jin, Z.: Natural language inference by tree-based convolution and heuristic matching. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Vol. 2: Short Papers), pp. 130–136 (2016)
Pakray, P., Bandyopadhyay, S., Gelbukh, A.F.: Binary-class and multi-class based textual entailment system. In: NTCIR (2013)
Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. 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), pp. 3973–3983 (2019)
Renjit, S., Idicula, S.: Natural language inference for Malayalam language using language agnostic sentence representation. PeerJ Comput. Sci. 7, e508 (2021)
Rocktäschel, T., Grefenstette, E., Hermann, K.M., Kociskỳ, T., Blunsom, P.: Reasoning about entailment with neural attention. Corr abs/1509.06664 (2015)
Sarkar, K.: Ks_ju@ dpil-fire2016: detecting paraphrases in Indian languages using multinomial logistic regression model. arXiv preprint arXiv:1612.08171 (2016)
Son, N.T., Phan, V.A., Nguyen, L.M.: Recognizing entailments in legal texts using sentence encoding-based and decomposable attention models. In: COLIEE@ ICAIL, pp. 31–42 (2017)
Sun, C., Liu, Y., Liu, B., Lin, L., et al.: Recognizing text entailment via bidirectional LSTM model with inner-attention. In: International Conference on Intelligent Computing, pp. 448–457. Springer (2017)
Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 1112–1122. Association for Computational Linguistics (2018). http://aclweb.org/anthology/N18-1101
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Renjit, S., Idicula, S.M. (2022). Textual Inference Identification in the Malayalam Language Using Convolutional Neural Network. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_20
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