Linguistic representations in multi-task neural networks for ellipsis resolution

Ola  Rønning; Daniel  Hardt; Anders Søgaard

Linguistic representations in multi-task neural networks for ellipsis resolution

Ola Rønning, Daniel Hardt, Anders Søgaard

Abstract

Sluicing resolution is the task of identifyingthe antecedent to a question ellipsis. Antecedentsare often sentential constituents, andprevious work has therefore relied on syntacticparsing, together with complex linguisticfeatures. A recent model instead used partialparsing as an auxiliary task in sequential neuralnetwork architectures to inject syntactic information.We explore the linguistic informationbeing brought to bear by such networks,both by defining subsets of the data exhibitingrelevant linguistic characteristics, and byexamining the internal representations of thenetwork. Both perspectives provide evidencefor substantial linguistic knowledge being deployedby the neural networks.

Originalsprog	Engelsk
Titel	Proceedings of the 2018 EMNLP Workshop BlackboxNLP : Analyzing and Interpreting Neural Networks for NLP
Forlag	Association for Computational Linguistics
Publikationsdato	2018
Sider	66–73
Status	Udgivet - 2018
Begivenhed	2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP - Brussels, Belgien Varighed: 1 nov. 2018 → 1 nov. 2018

Workshop

Workshop	2018 EMNLP Workshop BlackboxNLP
Land/Område	Belgien
By	Brussels
Periode	01/11/2018 → 01/11/2018

Citationsformater

Linguistic representations in multi-task neural networks for ellipsis resolution. / Rønning, Ola ; Hardt, Daniel ; Søgaard, Anders.
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics, 2018. s. 66–73.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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