Top-down Tree Long Short-Term Memory Networks

Xingxing Zhang, Liang Lu, Maria Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Long Short-Term Memory (LSTM) networks,a type of recurrent neural network with a more complex computational unit, have been successfully applied to a variety of sequence modeling tasks. In this paper we develop Tree Long Short-Term Memory (TREELSTM), a neural network model based on LSTM, which is designed to predict a tree rather than a linear sequence. TREELSTM defines the probability of a sentence by estimating the generation probability of its dependency tree. At each time step, a node is generated based on the representation of the generated subtree. We further enhance the modeling power of TREELSTM by explicitly representing the correlations between left and right dependents. Application of our model to the MSR sentence completion challenge achieves results beyond the current state of the art. We also report results on dependency parsing reranking achieving competitive performance.
Original languageEnglish
Title of host publicationThe 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
PublisherAssociation for Computational Linguistics
Pages310-320
Number of pages11
ISBN (Print)978-1-941643-91-4
Publication statusPublished - Jun 2016
Event15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - San Diego, United States
Duration: 12 Jun 201617 Jun 2016
http://naacl.org/naacl-hlt-2016/
http://naacl.org/naacl-hlt-2016/

Conference

Conference15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2016
CountryUnited States
CitySan Diego
Period12/06/1617/06/16
Internet address

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