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Communication Dans Un Congrès Année : 2017

Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks

Résumé

We introduce a constituency parser based on a bi-LSTM encoder adapted from re- cent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016), which can incorporate a lower level character bi- LSTM (Ballesteros et al., 2015; Plank et al., 2016). We model two important in- terfaces of constituency parsing with aux- iliary tasks supervised at the word level: (i) part-of-speech (POS) and morpholog- ical tagging, (ii) functional label predic- tion. On the SPMRL dataset, our parser obtains above state-of-the-art results on constituency parsing without requiring ei- ther predicted POS or morphological tags, and outputs labelled dependency trees.
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Dates et versions

hal-01522313 , version 1 (16-05-2017)

Identifiants

  • HAL Id : hal-01522313 , version 1

Citer

Maximin Coavoux, Benoît Crabbé. Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks. 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), Apr 2017, Valence, Spain. pp.331-336. ⟨hal-01522313⟩
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