Dialogue focus tracking for zero pronoun resolution

Rao, Sudha; Ettinger, Allyson; Daumé, Hal; Resnik, Philip

doi:10.3115/v1/n15-1052

Cited by 18 publications

(16 citation statements)

References 6 publications

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“…However, as shown in "Non-Fixed Error" case, there are still some ZPs can not be precisely predicted due to the misunderstanding of intentions of utterances. Thus, exploiting dialogue focus for ZP translation is our future work (Rao et al, 2015).…”

Section: Manual Evaluation On Translation Errorsmentioning

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: Manual Evaluation On Translation Errorsmentioning

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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“…More recently, Chen and Ng (2014b; have proposed unsupervised probabilistic AZP resolution models that rivaled their supervised counterparts in performance. While we aim to resolve anaphoric ZPs, Rao et al (2015) resolved deictic non-anaphoric ZPs, which "refer to salient entities in the environment such as the speaker, hearer or pragmatically accessible referent without requiring any introduction in the preceding text''.…”

Section: Related Workmentioning

confidence: 99%

Chinese Zero Pronoun Resolution with Deep Neural Networks

Chen¹,

Ng²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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While unsupervised anaphoric zero pronoun (AZP) resolvers have recently been shown to rival their supervised counterparts in performance, it is relatively difficult to scale them up to reach the next level of performance due to the large amount of feature engineering efforts involved and their ineffectiveness in exploiting lexical features. To address these weaknesses, we propose a supervised approach to AZP resolution based on deep neural networks, taking advantage of their ability to learn useful task-specific representations and effectively exploit lexical features via word embeddings. Our approach achieves stateof-the-art performance when resolving the Chinese AZPs in the OntoNotes corpus.

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“…They applied their classifier separately to each word in each testing message. Rao et al (2015) addressed a simpler version of dropped pronoun recovery in which only the person number (first, second, or third) of the dropped pronouns need be restored. They used the approach in Cai et al (2011) to identify dropped pronoun slots.…”

Section: Related Workmentioning

confidence: 99%

“…Given the substantial difference between formally written text and SMS, further study is warranted on SMS. Very recently, two studies Rao et al (2015) and Yang et al (2015) have addressed Chinese dropped personal pronoun recovery in SMS. These studies were conducted simultaneously to, and independently of, our research.…”

Section: Related Workmentioning

confidence: 99%

“…Among other sources of error, the brevity of SMS sometimes made the determination of dropped pronoun person number difficult. Rao, Ettinger, Daume III and Resnik (2015) address this problem by considering dialog threads of SMS. Since our dataset did not contain dialog threads, we focused only on information that could be derived from messages treated individually.…”

Section: Introductionmentioning

confidence: 99%

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Dropped personal pronoun recovery in Chinese SMS

Giannella¹,

Winder²,

Petersen³

2017

Nat. Lang. Eng.

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In written Chinese, personal pronouns are commonly dropped when they can be inferred from context. This practice is particularly common in informal genres like Short Message Service messages sent via cell phones. Restoring dropped personal pronouns can be a useful preprocessing step for information extraction. Dropped personal pronoun recovery can be divided into two subtasks: (1) detecting dropped personal pronoun slots and (2) determining the identity of the pronoun for each slot. We address a simpler version of restoring dropped personal pronouns wherein only the person numbers are identified. After applying a word segmenter, we used a linear-chain conditional random field to predict which words were at the start of an independent clause. Then, using the independent clause start information, as well as lexical and syntactic information, we applied a conditional random field or a maximum-entropy classifier to predict whether a dropped personal pronoun immediately preceded each word and, if so, the person number of the dropped pronoun. We conducted a series of experiments using a manually annotated corpus of Chinese Short Message Service. Our approaches substantially outperformed a rule-based approach based partially on rules developed by Chung and Gildea (2010, Effects of Empty Categories on Machine Translation. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. pp. 636–45). Our approaches also outperformed (though by a considerably smaller margin) a machine-learning approach based closely on work by Yang, Liu, and Xue in (2015, Recovering Dropped Pronouns from Chinese Text Messages. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL). Association for Computational Linguistics. pp. 309–13). Features derived from parsing largely did not help our approaches. We conclude that, given independent clause start information, the parse information we used was largely superfluous for identifying dropped personal pronouns.

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Dialogue focus tracking for zero pronoun resolution

Cited by 18 publications

References 6 publications

One Model to Learn Both: Zero Pronoun Prediction and Translation

One Model to Learn Both: Zero Pronoun Prediction and Translation

Chinese Zero Pronoun Resolution with Deep Neural Networks

Dropped personal pronoun recovery in Chinese SMS

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