Abstract
Natural Language Inference (NLI) is the task of detecting relations such as entailment, contradiction and paraphrase in pairs of sentences. With the recent release of the ASSIN corpus, NLI in Portuguese is now getting more attention. However, published results on ASSIN have not explored syntactic structure, neither combined word embedding metrics with other types of features. In this work, we sought to remedy this gap, proposing a new model for NLI that achieves 0.72 F\(_1\) score on ASSIN, setting a new state of the art. Our feature analysis shows that word embeddings and syntactic knowledge are both important to achieve such results.
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Notes
- 1.
Available at nilc.icmc.usp.br/assin/.
- 2.
More information at http://universaldependencies.org.
- 3.
More information at http://spacy.io.
- 4.
The DELAF-PB dictionary maps inflected word forms to lemmas, according to their part-of-speech tag. More information at http://www.nilc.icmc.usp.br/nilc/projects/unitex-pb/web/dicionarios.html.
- 5.
More information at http://www.nltk.org.
References
Agirre, E., et al.: SemEval-2015 Task 2: semantic textual similarity, English, Spanish and pilot on interpretability. In: Proceedings of the 9th International Workshop on Semantic Evaluation, SemEval 2015, pp. 252–263. Association for Computational Linguistics (2015)
Alves, A.O., Oliveira, H.G., Rodrigues, R.: ASAPP e Reciclagem no ASSIN: Alinhamento Semântico Automático de Palavras aplicado ao Português. Linguamática 8(2), 43–58 (2016)
Barbosa, L., Cavalin, P., Martins, B., Guimarães, V., Kormaksson, M.: Blue Man Group no ASSIN: Usando Representações Distribuídas para Similaridade Semântica e Inferência Textual. Linguamática 8(2), 15–22 (2016)
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP, pp. 632–642. The Association for Computational Linguistics (2015)
Chen, Q., Zhu, X., Ling, Z.H., Wei, S., Jiang, H., Inkpen, D.: Enhanced LSTM for natural language inference. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Long Papers, vol. 1, pp. 1657–1668. Association for Computational Linguistics (2017)
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, vol. 3944, pp. 177–190. Springer, Heidelberg (2006). https://doi.org/10.1007/11736790_9
Dagan, I., Roth, D., Sammons, M., Zanzotto, F.M.: Recognizing Textual Entailment: Models and Applications. Synthesis Lectures on Human Language Technologies. Morgan & Claypool, San Rafael (2013)
Feitosa, D.B., Pinheiro, V.C.: Análise de medidas de similaridade semântica na tarefa de reconhecimento de implicação textual. In: Proceedings of Symposium in Information and Human Language Technology (2017)
Fialho, P., Marques, R., Martins, B., Coheur, L., Quaresma, P.: INESC-ID no ASSIN: measuring semantic similarity and recognizing textual entailment. Linguamática 8(2), 33–42 (2016)
Fonseca, E.R., dos Santos, L.B., Criscuolo, M., Aluísio, S.M.: Visão Geral da Avaliação de Similaridade Semântica e Inferência Textual. Linguamática 8(2), 3–13 (2016)
Hartmann, N.S.: Solo queue no ASSIN: mix of a traditional and an emerging approaches. Linguamática 8(2), 59–64 (2016)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014)
Marelli, M., Menini, S., Baroni, M., Bentivogli, L., Bernardi, R., Zamparelli, R.: A SICK cure for the evaluation of compositional distributional semantic models. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation, pp. 216–223. European Language Resources Association (ELRA) (2014)
de Paiva, V., Rademaker, A., de Melo, G.: OpenWordNet-PT: an open Brazilian WordNet for reasoning. In: Proceedings of the 24th International Conference on Computational Linguistics, COLING 2012, pp. 353–360 (2012)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing, EMNLP, pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14-1162
Rocha, G., Cardoso, L.H.: Recognizing textual entailment: challenges in the Portuguese language. Information 9(4), 76 (2018)
Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., Zhang, C.: DiSAN: directional self-attention network for RNN/CNN-free language understanding. ArXiv e-prints (2017)
Zanoli, R., Colombo, S.: A transformation-driven approach for recognizing textual entailment. Nat. Lang. Eng. 23(4), 507–534 (2016)
Zhang, K., Shasha, D.: Simple fast algorithms for the editing distance between trees and related problems. SIAM J. Comput. 18, 1245–1262 (1989)
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This work was supported by FAPESP grant 2013/22973-0.
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Fonseca, E., Aluísio, S.M. (2018). Syntactic Knowledge for Natural Language Inference in Portuguese. In: Villavicencio, A., et al. Computational Processing of the Portuguese Language. PROPOR 2018. Lecture Notes in Computer Science(), vol 11122. Springer, Cham. https://doi.org/10.1007/978-3-319-99722-3_25
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