Recovering dropped pronouns in Chinese conversations via modeling their referents

22188

Authors

nan

Abstract

AbstractPronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context. Recovering dropped pronouns is essential to applications such as Information Extraction where the referents of these dropped pronouns need to be resolved, or Machine Translation when Chinese is the source language. In this work, we present a novel end-to-end neural network model to recover dropped pronouns in conversational data. Our model is based on a structured attention mechanism that models the referents of dropped pronouns utilizing both sentence-level and word-level information. Results on three different conversational genres show that our approach achieves a significant improvement over the current state of the art.

Keywords

nan

Full Content

Proceedings of NAACL-HLT 2019, pages 892–901 Minneapolis, Minnesota, June 2 - June 7, 2019. c⃝2019 Association for Computational Linguistics 892 �� ∗� �� † �� †�� ∗ �� 它 �� …

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Publication Details

Paper ID:: 22188
Year:: 2019
Publication:: TIPSTER
Publication Type:: conf
DOI:: nan

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