@inproceedings{88508d7f11384100a139149af8c4120c,
title = "Zero pronoun resolution with attention-based neural network",
abstract = "Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the zero pronouns since they are simply gaps that contain no actual content. To better utilize contexts of the zero pronouns, we here introduce the self-attention mechanism for encoding zero pronouns. With the help of the multiple hops of attention, our model is able to focus on some informative parts of the associated texts and therefore produces an efficient way of encoding the zero pronouns. In addition, an attention-based recurrent neural network is proposed for encoding candidate antecedents by their contents. Experiment results are encouraging: our proposed attention-based model gains the best performance on the Chinese portion of the OntoNotes corpus, substantially surpasses existing Chinese zero pronoun resolution baseline systems.",
author = "Qingyu Yin and Yu Zhang and Weinan Zhang and Ting Liu and Wang, \{William Yang\}",
note = "Publisher Copyright: {\textcopyright} 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.; 27th International Conference on Computational Linguistics, COLING 2018 ; Conference date: 20-08-2018 Through 26-08-2018",
year = "2018",
language = "英语",
series = "COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings",
publisher = "Association for Computational Linguistics (ACL)",
pages = "13--23",
editor = "Bender, \{Emily M.\} and Leon Derczynski and Pierre Isabelle",
booktitle = "COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings",
address = "澳大利亚",
}