Linguistic representations in multi-task neural networks for ellipsis resolution

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
OriginalsprogEngelsk
TitelProceedings of the 2018 EMNLP Workshop BlackboxNLP : Analyzing and Interpreting Neural Networks for NLP
ForlagAssociation for Computational Linguistics
Publikationsdato2018
Sider66–73
StatusUdgivet - 2018
Begivenhed2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP - Brussels, Belgien
Varighed: 1 nov. 20181 nov. 2018

Workshop

Workshop2018 EMNLP Workshop BlackboxNLP
LandBelgien
ByBrussels
Periode01/11/201801/11/2018

ID: 214759805