Linguistic representations in multi-task neural networks for ellipsis resolution

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 2018 EMNLP Workshop BlackboxNLP : Analyzing and Interpreting Neural Networks for NLP
PublisherAssociation for Computational Linguistics
Publication date2018
Publication statusPublished - 2018
Event2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP - Brussels, Belgium
Duration: 1 Nov 20181 Nov 2018


Workshop2018 EMNLP Workshop BlackboxNLP

ID: 214759805