Chinese Zero Pronoun Resolution: A Joint Unsupervised Discourse-Aware Model Rivaling State-of-the-Art Resolvers

Liu

et al. 2019

Front. Comput. Sci.

Dropped pronouns (DPs) are ubiquitous in pro-drop languages like Chinese, Japanese etc. Previous work mainly focused on painstakingly exploring the empirical features for DPs recovery. In this paper, we propose a neural recovery machine (NRM) to model and recover DPs in Chinese, so that to avoid the non-trivial feature engineering process. The experimental results show that the proposed NRM significantly outperforms the state-of-the-art approaches on both two heterogeneous datasets. Further experiment results of Chinese zero pronoun (ZP) resolution show that the performance of ZP resolution can also be improved by recovering the ZPs to DPs.

Section: Zero Pronoun Resolutionmentioning

confidence: 99%

Neural recovery machine for Chinese dropped pronoun

Liu

et al. 2019

Front. Comput. Sci.

“…Five recent zero pronoun resolution systems are employed as our baselines, namely, Zhao and Ng (2007), Chen and Ng (2015), Chen and Ng (2016), Yin et al (2017a) and Yin et al (2017b). The first of them is machine learning-based, the second is the unsupervised and the other ones are all deep learning models.…”

Section: Baselines and Experiments Settingsmentioning

confidence: 99%

Deep Reinforcement Learning for Chinese Zero Pronoun Resolution

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

et al. 2018

Deep neural network models for Chinese zero pronoun resolution learn semantic information for zero pronoun and candidate antecedents, but tend to be short-sightedthey often make local decisions. They typically predict coreference chains between the zero pronoun and one single candidate antecedent one link at a time, while overlooking their long-term influence on future decisions. Ideally, modeling useful information of preceding potential antecedents is critical when later predicting zero pronoun-candidate antecedent pairs. In this study, we show how to integrate local and global decision-making by exploiting deep reinforcement learning models. With the help of the reinforcement learning agent, our model learns the policy of selecting antecedents in a sequential manner, where useful information provided by earlier predicted antecedents could be utilized for making later coreference decisions. Experimental results on OntoNotes 5.0 dataset show that our technique surpasses the state-of-the-art models. * Corresponding author. ([Litigant LiYading] not only shows φ1 willing of acception, but also φ2 hopes that there should be someone in charge of it.)

“…They first recover each ZP into ten overt pronouns and then apply a ranking model to rank the antecedents. Chen and Ng (2015) propose an end-to-end unsupervised probabilistic model, utilizing a salience model to capture discourse information. In recent years, Chen and Ng (2016) develop a deep neural network approach to learn useful task-specific representations and effectively exploit lexical features through word embeddings.…”

Section: Zero Pronoun Resolutionmentioning

confidence: 99%

Chinese Zero Pronoun Resolution with Deep Memory Network

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

et al. 2017

Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.