Unsupervised Discourse Constituency Parsing Using Viterbi EM

被引：10

作者：

Nishida, Noriki ^{[1
]}

Nakayama, Hideki ^{[1
]}

机构：

[1] Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo, Japan

来源：

TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS | 2020年 / 8卷 / 08期

关键词：

57;

D O I：

10.1162/tacl_a_00312

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we introduce an unsupervised discourse constituency parsing algorithm. We use Viterbi EM with a margin- based criterion to train a span-based discourse parser in an unsupervised manner. We also propose initialization methods for Viterbi training of discourse constituents based on our prior knowledge of text structures. Experimental results demonstrate that our unsupervised parser achieves comparable or even superior performance to fully supervised parsers. We also investigate discourse constituents that are learned by our method.

引用

页码：215 / 230

页数：16

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