Papers with ZPs

4 papers
One Model to Learn Both: Zero Pronoun Prediction and Translation (D19-1)

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Challenge: Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach: They propose a unified and discourse-aware ZP translation approach for neural MT models . they jointly learn to predict and translate ZPs in an end-to-end manner .
Outcome: The proposed method improves translation performance and ZP prediction accuracy over baseline models and external models.
A Survey on Zero Pronoun Translation (2023.acl-long)

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Challenge: Zero pronouns (ZPs) are often omitted in pro-drop languages, but should be recalled in non-pro-drop language.
Approach: They propose to analyze the literature on zero pronoun translation after the neural revolution . they uncover that data limitation causes learning bias in languages and domains .
Outcome: The proposed method and methods are compared to other models and evaluation metrics on different benchmarks.
Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation (2021.emnlp-main)

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Challenge: ZP-annotated natural language generation (NLG) corpora are scarce in pro-drop languages . despite efforts to bridge the discrepancy between human and machine, zero pronouns still persist in pro -drop tasks.
Approach: They propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks.
Outcome: The proposed approach can improve translation, question answering, and summarization tasks.
GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)

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Challenge: ZPs are often omitted when they can be pragmatically or grammatically inferred from intraand inter-sentential contexts.
Approach: They propose a benchmark testset for target evaluation on Chinese-English ZP translation.
Outcome: The proposed testset covers five genres and identifies current challenges for evaluation.

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