Chinese Zero Pronoun Resolution with Deep Memory Network

Yin, Qinye; Yu, Zhongbo; Zhang, Weinan; Liu, Ting

doi:10.18653/v1/d17-1135

Cited by 35 publications

(29 citation statements)

References 20 publications

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“…ZP Prediction and Translation ZP resolution is a challenging task which needs lexical, syntactic, discourse knowledge. Previous studies have been conducted to improves the performance of ZP resolution for different pro-drop languages (Kong and Zhou, 2010;Chen and Ng, 2013;Park et al, 2015;Yin et al, 2017). However, directly using results of external ZP resolution systems for translation task shows limited improvements (Chung and Gildea, 2010;Le Nagard and Koehn, 2010;Taira et al, 2012;Xiang et al, 2013), since such external systems are trained on small-scale data that is non-homologous to MT.…”

Section: Related Workmentioning

confidence: 99%

One Model to Learn Both: Zero Pronoun Prediction and Translation

Wang¹,

Tu²,

Wang³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should be recalled in non-pro-drop languages. This discourse phenomenon poses a significant challenge for machine translation (MT) when translating texts from pro-drop to non-pro-drop languages. In this paper, we propose a unified and discourseaware ZP translation approach for neural MT models. Specifically, we jointly learn to predict and translate ZPs in an end-to-end manner, allowing both components to interact with each other. In addition, we employ hierarchical neural networks to exploit discourse-level context, which is beneficial for ZP prediction and thus translation. Experimental results on both Chinese⇒English and Japanese⇒English data show that our approach significantly and accumulatively improves both translation performance and ZP prediction accuracy over not only baseline but also previous works using external ZP prediction models. Extensive analyses confirm that the performance improvement comes from the alleviation of different kinds of errors especially caused by subjective ZPs.

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Section: Related Workmentioning

confidence: 99%

One Model to Learn Both: Zero Pronoun Prediction and Translation

Wang¹,

Tu²,

Wang³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…For Chinese, where zero anaphors are often used, neural network-based approaches (Chen and Ng, 2016;Yin et al, 2017) outperformed conventional machine learning approaches (Zhao and Ng, 2007).…”

Section: Related Workmentioning

confidence: 99%

Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis

Shibata

Kurohashi

2018

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

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Predicate argument structure analysis is a task of identifying structured events. To improve this field, we need to identify a salient entity, which cannot be identified without performing coreference resolution and predicate argument structure analysis simultaneously. This paper presents an entity-centric joint model for Japanese coreference resolution and predicate argument structure analysis. Each entity is assigned an embedding, and when the result of both analyses refers to an entity, the entity embedding is updated. The analyses take the entity embedding into consideration to access the global information of entities. Our experimental results demonstrate the proposed method can improve the performance of the intersentential zero anaphora resolution drastically, which is a notoriously difficult task in predicate argument structure analysis.

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“…F 1 P@1 POSNet-D+Yin et al (2018a) 92.9 69.7 POSNet-D+Yin et al (2018b) 92.9 69.9 POSNet 95.4 71.7 Table 5: Results of the end-to-end evaluation for coreference resolution on the CQA dataset Model F 1 Zhao and Ng (2007) 41.5 Chen and Ng (2016) 52.2 Yin et al (2017) 54.9 Liu et al (2016) 55.3 Yin et al (2018b) 57.3 Yin et al (2018a) 57.2 POSNet-R (raw) 52.1 POSNet-R (pretrained on CQA) 58.1 Table 6: Results of mention candidate ranking for zero pronouns on the CONLL2012 dataset For the CONLL2012 dataset, the result is shown in Table 6. Following Yin et al (2018b), we add the features from existing work on zero anaphora resolution into the fully connection layer.…”

Section: Modelmentioning

confidence: 99%

End-to-End Neural Context Reconstruction in Chinese Dialogue

Yang¹,

Qiao²,

Qin³

et al. 2019

Proceedings of the First Workshop on NLP for Conversational AI

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We tackle the problem of context reconstruction in Chinese dialogue, where the task is to replace pronouns, zero pronouns, and other referring expressions with their referent nouns so that sentences can be processed in isolation without context. Following a standard decomposition of the context reconstruction task into referring expression detection and coreference resolution, we propose a novel end-toend architecture for separately and jointly accomplishing this task. Key features of this model include POS and position encoding using CNNs and a novel pronoun masking mechanism. One perennial problem in building such models is the paucity of training data, which we address by augmenting previouslyproposed methods to generate a large amount of realistic training data. The combination of more data and better models yields accuracy higher than the state-of-the-art method in coreference resolution and end-to-end context reconstruction.

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Chinese Zero Pronoun Resolution with Deep Memory Network

Cited by 35 publications

References 20 publications

One Model to Learn Both: Zero Pronoun Prediction and Translation

One Model to Learn Both: Zero Pronoun Prediction and Translation

Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis

End-to-End Neural Context Reconstruction in Chinese Dialogue

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