@inproceedings{yang-etal-2019-recovering,
title = "Recovering dropped pronouns in {C}hinese conversations via modeling their referents",
author = "Yang, Jingxuan and
Tong, Jianzhuo and
Li, Si and
Gao, Sheng and
Guo, Jun and
Xue, Nianwen",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1095",
doi = "10.18653/v1/N19-1095",
pages = "892--901",
abstract = "Pronouns 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.",
}
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<abstract>Pronouns 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.</abstract>
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%0 Conference Proceedings
%T Recovering dropped pronouns in Chinese conversations via modeling their referents
%A Yang, Jingxuan
%A Tong, Jianzhuo
%A Li, Si
%A Gao, Sheng
%A Guo, Jun
%A Xue, Nianwen
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F yang-etal-2019-recovering
%X Pronouns 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.
%R 10.18653/v1/N19-1095
%U https://aclanthology.org/N19-1095
%U https://doi.org/10.18653/v1/N19-1095
%P 892-901
Markdown (Informal)
[Recovering dropped pronouns in Chinese conversations via modeling their referents](https://aclanthology.org/N19-1095) (Yang et al., NAACL 2019)
ACL
- Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, and Nianwen Xue. 2019. Recovering dropped pronouns in Chinese conversations via modeling their referents. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 892–901, Minneapolis, Minnesota. Association for Computational Linguistics.