| 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. |
Similar Papers
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. |
Learning to Jointly Translate and Predict Dropped Pronouns with a Shared Reconstruction Mechanism (D18-1)
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| Challenge: | Pronouns are often omitted in pro-drop languages, such as Chinese . this leads to various translation problems in terms of completeness, syntax and semantics . |
| Approach: | They propose a reconstruction-based approach to alleviate dropped pronoun (DP) translation problems for neural machine translation models by employing a shared reconstructor and a joint learning approach. |
| Outcome: | The proposed approach improves translation performance and accuracy of DP predictions. |
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. |
Zero Pronouns Identification based on Span prediction (2021.acl-srw)
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| Challenge: | Pro-drop languages allow omissions of essential phrases or arguments . the presence of zero-pronouns affects downstream tasks of NLP . |
| Approach: | They propose a query-based method to identify zero-pronoun arguments . they use Japanese and Chinese datasets to evaluate the method . |
| Outcome: | The proposed method surpasses the sequence labeling baseline on Japanese and Chinese datasets. |
GuoFeng: A Benchmark for Zero Pronoun Recovery and Translation (2022.emnlp-main)
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Mingzhou Xu, Longyue Wang, Derek F. Wong, Hongye Liu, Linfeng Song, Lidia S. Chao, Shuming Shi, Zhaopeng Tu
| 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. |
Context-Aware Neural Machine Translation Learns Anaphora Resolution (P18-1)
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| Challenge: | Standard machine translation systems process sentences in isolation and ignore extra-sentential information. |
| Approach: | They propose a context-aware neural machine translation model that controls flow of information from extended context to the translation model. |
| Outcome: | The proposed model improves on an English-Russian subtitles dataset over its context-agnostic version (+0.7) and over simple concatenation of context and source sentences (+0.6). |
Autocorrect in the Process of Translation — Multi-task Learning Improves Dialogue Machine Translation (2021.naacl-industry)
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| Challenge: | Existing neural machine translation models are not able to translate dialogues in real life scenarios. |
| Approach: | They propose a joint learning method to identify omission and typos and utilize context to translate dialogue utterances. |
| Outcome: | The proposed method improves translation quality by 3.2 BLEU over baselines and recovers omitted pronouns by 47.16%. |
PROSE: A Pronoun Omission Solution for Chinese-English Spoken Language Translation (2023.emnlp-main)
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| Challenge: | Pro-drop (‘pronoun-dropping’) language requires NMT systems to recover omitted pronouns, but this task lacks sufficient datasets for benchmarking . |
| Approach: | They propose a benchmarking method that leverages the semantic embedding of dropped pronouns to augment training pairs to alleviate the negative impact introduced by pro-drop . |
| Outcome: | The proposed method outperforms existing methods regarding omitted pronoun retrieval and overall translation quality on four Chinese-English translation corpora. |
Token Drop mechanism for Neural Machine Translation (2020.coling-main)
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| Challenge: | Neural machine translation models are vulnerable to unfamiliar inputs. |
| Approach: | They propose to drop tokens of the input sentences to improve generalization and avoid overfitting for the NMT model. |
| Outcome: | The proposed approach improves on Chinese-English and English-Romanian benchmarks and achieves significant performance improvements over baselines. |
Zero Pronoun Resolution with Attention-based Neural Network (C18-1)
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| Challenge: | Recent neural network methods for zero pronoun resolution use contextual information to encode the zero pronomins since they contain no actual content. |
| Approach: | They propose a self-attention mechanism for encoding zero pronouns that focus on some informative parts of the associated texts and produce an efficient way of encode them. |
| Outcome: | The proposed model significantly surpasses existing Chinese zero pronoun resolution baseline systems. |