Linguistic Representations in Multi-task Neural Networks for Ellipsis Resolution

Ola Rønning, Daniel Hardt, Anders Søgaard

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Abstract

Sluicing resolution is the task of identifying the antecedent to a question ellipsis. Antecedents are often sentential constituents, and previous work has therefore relied on syntactic parsing, together with complex linguistic features. A recent model instead used partial parsing as an auxiliary task in sequential neural network architectures to inject syntactic information. We explore the linguistic information being brought to bear by such networks, both by defining subsets of the data exhibiting relevant linguistic characteristics, and by examining the internal representations of the network. Both perspectives provide evidence for substantial linguistic knowledge being deployed by the neural networks.
Original languageEnglish
Title of host publicationProceedings of the 2018 EMNLP Workshop BlackboxNLP : Analyzing and Interpreting Neural Networks for NLP
EditorsTal Linzen, Grzegorz Chrupała, Afra Alishahi
Number of pages8
Place of PublicationBrussels
PublisherAssociation for Computational Linguistics
Publication date2018
Pages66-73
Publication statusPublished - 2018
Event2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018
http://emnlp2018.org/

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing
LocationSquare Meeting Center
CountryBelgium
CityBrussels
Period31/10/201804/11/2018
Internet address

Cite this

Rønning, O., Hardt, D., & Søgaard, A. (2018). Linguistic Representations in Multi-task Neural Networks for Ellipsis Resolution. In T. Linzen, G. Chrupała, & A. Alishahi (Eds.), Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (pp. 66-73). Association for Computational Linguistics.