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

关键词：

D O I：

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

页码：612 / 618

页数：7

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