Work

Morphological Inflection Generation with Hard Monotonic Attention

Year: 2017

Type: conference-paper

Abstract: 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 nonneural 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.

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p17-1183

Authors: Roee Aharoni, Yoav Goldberg

Institution: Bar-Ilan University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Intel Collaboration Research Institute for Computational Intelligence

OpenAlex ID: W2625720409