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dc.contributor.advisorLapata, Maria
dc.contributor.advisorLopez, Adam
dc.contributor.authorDong, Li
dc.date.accessioned2019-03-18T11:00:22Z
dc.date.available2019-03-18T11:00:22Z
dc.date.issued2019-07-01
dc.identifier.urihttp://hdl.handle.net/1842/35587
dc.description.abstractLanguage is the primary and most natural means of communication for humans. The learning curve of interacting with various devices and services (e.g., digital assistants, and smart appliances) would be greatly reduced if we could talk to machines using human language. However, in most cases computers can only interpret and execute formal languages. In this thesis, we focus on using neural models to build natural language interfaces which learn to map naturally worded expressions onto machineinterpretable representations. The task is challenging due to (1) structural mismatches between natural language and formal language, (2) the well-formedness of output representations, (3) lack of uncertainty information and interpretability, and (4) the model coverage for language variations. In this thesis, we develop several flexible neural architectures to address these challenges. We propose a model based on attention-enhanced encoder-decoder neural networks for natural language interfaces. Beyond sequence modeling, we propose a tree decoder to utilize the compositional nature and well-formedness of meaning representations, which recursively generates hierarchical structures in a top-down manner. To model meaning at different levels of granularity, we present a structure-aware neural architecture which decodes semantic representations following a coarse-to-fine procedure. The proposed neural models remain difficult to interpret, acting in most cases as a black box. We explore ways to estimate and interpret the model’s confidence in its predictions, which we argue can provide users with immediate and meaningful feedback regarding uncertain outputs. We estimate confidence scores that indicate whether model predictions are likely to be correct. Moreover, we identify which parts of the input contribute to uncertain predictions allowing users to interpret their model. Model coverage is one of the major reasons resulting in uncertainty of natural language interfaces. Therefore, we develop a general framework to handle the many different ways natural language expresses the same information need. We leverage external resources to generate felicitous paraphrases for the input, and then feed them to a neural paraphrase scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. The model components are trained end-to-end using supervision signals provided by the target task. Experimental results show that the proposed neural models can be easily ported across tasks. Moreover, the robustness of natural language interfaces can be enhanced by considering the output well-formedness, confidence modeling, and improving model coverage.en
dc.contributor.sponsorotheren
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionCheng, J., Dong, L., and Lapata, M. (2016). Long short-term memory-networks for machine reading. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 551–561, Austin, Texas. Association for Computational Linguistics.en
dc.relation.hasversionDong, L., Huang, S., Wei, F., Lapata, M., Zhou, M., and Xu, K. (2017a). Learning to generate product reviews from attributes. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 623–632, Valencia, Spain. Association for Computational Linguistics.en
dc.relation.hasversionDong, L. and Lapata, M. (2016). Language to logical form with neural attention. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 33–43, Berlin, Germany.en
dc.relation.hasversionDong, L. and Lapata, M. (2018). Coarse-to-fine decoding for neural semantic parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 731–742. Association for Computational Linguistics.en
dc.relation.hasversionDong, L., Mallinson, J., Reddy, S., and Lapata, M. (2017b). Learning to paraphrase for question answering. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 875–886, Copenhagen, Denmark. Association for Computational Linguistics.en
dc.relation.hasversionDong, L., Quirk, C., and Lapata, M. (2018). Confidence modeling for neural semantic parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 743–753. Association for Computational Linguistics.en
dc.relation.hasversionDong, L., Wei, F., Sun, H., Zhou, M., and Xu, K. (2015a). A hybrid neural model for type classification of entity mentions. In Proceedings of the 24th International Conference on Artificial Intelligence, pages 1243–1249.en
dc.relation.hasversionDong, L., Wei, F., Zhou, M., and Xu, K. (2015b). Question answering over freebase with multi-column convolutional neural networks. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pages 260–269, Beijing, China. Association for Computational Linguistics.en
dc.subjectnatural language interfaceen
dc.subjectquestion answeringen
dc.subjectnatural language processingen
dc.titleLearning natural language interfaces with neural modelsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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