Morphological Inflection Generation with Hard Monotonic Attention

被引：51

作者：

Aharoni, Roee ^{[1
]}

Goldberg, Yoav ^{[1
]}

机构：

[1] Bar Ilan Univ, Dept Comp Sci, Ramat Gan, Israel

来源：

PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 | 2017年

关键词：

D O I：

10.18653/v1/P17-1183

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection. We evaluate the model on three previously studied morphological inflection generation datasets and show that it provides state of the art results in various setups compared to previous neural and non-neural approaches. Finally we present an analysis of the continuous representations learned by both the hard and soft attention (Bahdanau et al., 2015) models for the task, shedding some light on the features such models extract.

引用

页码：2004 / 2015

页数：12