An Empirical Study of Building a Strong Baseline for Constituency Parsing

被引:0
|
作者
Suzuki, Jun [1 ]
Takase, Sho [1 ]
Kamigaito, Hidetaka [1 ]
Morishita, Makoto [1 ]
Nagata, Masaaki [1 ]
机构
[1] NTT Corp, NTT Commun Sci Labs, 2-4 Hikaridai, Seika, Kyoto 6190237, Japan
来源
PROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | 2018年
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暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers' performance without requiring explicit task-specific knowledge or architecture of constituent parsing.
引用
收藏
页码:612 / 618
页数:7
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