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TitreMulti-Task Learning for Japanese Predicate Argument Structure Analysis
Année2019
Type Article dans proceedings
Auteur
 
LieuMinneapolis, Minnesota
Editeur scientifique
Nom de la publicationProceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
EditionAssociation for Computational Linguistics
urlhttps://www.aclweb.org/anthology/N19-1344
Bibtexdata/2019/19102480543.bib
RésuméAn event-noun is a noun that has an argument structure similar to a predicate. Recent works, including those considered state-of-the-art, ignore event-nouns or build a single model for solving both Japanese predicate argument structure analysis (PASA) and event-noun argument structure analysis (ENASA). However, because there are interactions between predicates and event-nouns, it is not sufficient to target only predicates. To address this problem, we present a multi-task learning method for PASA and ENASA. Our multi-task models improved the performance of both tasks compared to a single-task model by sharing knowledge from each task. Moreover, in PASA, our models achieved state-of-the-art results in overall F1 scores on the NAIST Text Corpus. In addition, this is the first work to employ neural networks in ENASA.

2018/02/16
blin -- ehess . fr