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• W3212781411 abstract "Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora. In this case, we propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. Notably, we frame the recovery process in a task-supervised fashion where the ZP representation recovering capability is learned during the NLG task learning process, thus our method does not require NLG corpora annotated with ZPs. For system enhancement, we learn an adversarial bot to adjust our model outputs to alleviate the error propagation caused by mis-recovered ZPs. Experiments on three document-level NLG tasks, i.e., machine translation, question answering, and summarization, show that our approach can improve the performance to a great extent, and the improvement on pronoun translation is very impressive." @default.
• W3212781411 created "2021-11-22" @default.
• W3212781411 creator A5012794465 @default.
• W3212781411 creator A5071953338 @default.
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• W3212781411 date "2021-01-01" @default.
• W3212781411 modified "2023-10-13" @default.
• W3212781411 title "Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation" @default.
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• W3212781411 doi "https://doi.org/10.18653/v1/2021.emnlp-main.197" @default.
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