| 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. |
Similar Papers
Deep Reinforcement Learning for Chinese Zero Pronoun Resolution (P18-1)
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| Challenge: | Recent models for zero pronoun resolution in Chinese are short-sighted and do not capture semantic information for zeros and candidate antecedents. |
| Approach: | They propose to integrate a deep reinforcement learning approach to Chinese zero pronoun resolution. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods in three experimental settings. |
Cross-lingual Zero Pronoun Resolution (2020.lrec-1)
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| Challenge: | In pronoun-dropping languages, predicate arguments are not realized instead of being realized as overt pronounos. |
| Approach: | They propose a BERT-based model for zero pronoun resolution in Arabic and Chinese . they also evaluate BERT feature extraction and fine-tune models on the task . |
| Outcome: | The proposed model outperforms the state-of-the-art model for Arabic and Chinese on OntoNotes 5.0. |
Evaluation Dataset for Zero Pronoun in Japanese to English Translation (2020.lrec-1)
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| Challenge: | In natural language, we often omit some words that are easily understandable from the context. |
| Approach: | They propose to use a dataset to evaluate whether translation models can resolve zero pronoun problems in Japanese to English translations. |
| Outcome: | The proposed model can resolve the zero pronoun problem in Japanese to English translations. |
ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT (2020.acl-main)
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| Challenge: | Zero pronoun recovery and resolution aim at recovering the dropped pronounce and pointing out its anaphoric mentions. |
| Approach: | They propose to solve two tasks together to recover the dropped pronoun and point out its anaphoric mentions. |
| Outcome: | The proposed model outperforms previous state of the arts benchmarks on two benchmarks. |
Recovering dropped pronouns in Chinese conversations via modeling their referents (N19-1)
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| Challenge: | Pronouns are often dropped in conversational genres as their referents can be easily understood from context. |
| Approach: | They propose an end-to-end neural network model to recover dropped pronouns in conversational data. |
| Outcome: | The proposed model improves on three different conversational genres. |
Knowledge-aware Pronoun Coreference Resolution (P19-1)
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| Challenge: | Existing models for pronoun coreference resolution only use triplets, the most common format for knowledge graphs. |
| Approach: | They propose a model that leverages different types of knowledge to resolve pronoun coreference with a neural model. |
| Outcome: | The proposed model outperforms state-of-the-art baselines on two datasets from different domains. |
Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models (2025.coling-main)
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| Challenge: | In Context-aware Machine Translation, the context sentences are available to the system and can be used to maintain coherence of translation and resolve ambiguities. |
| Approach: | They investigate the role of attention heads in Context-aware Machine Translation models for pronoun disambiguation in the English-to-German and English- to-French directions. |
| Outcome: | The attention heads influence the models' ability to disambiguate pronouns in the English-to-German and English- to-French directions. |
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution (2021.emnlp-main)
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| Challenge: | Masked language models have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). |
| Approach: | They propose a pretraining task that trains MLMs on anaphoric relations with explicit supervision and a finetuning method that remedies a notorious discrepancy. |
| Outcome: | The proposed method improves zero anaphora resolution in Japanese ZAR . it uses a pretrain task and finetuning task to correct the discrepancy . |
Annotating Zero Anaphora for Question Answering (L18-1)
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| Challenge: | a large dataset of zero pronouns has been constructed to identify adjunct zero anaphoras . a lack of a dataset covering them has limited our ability to annotate them exhaustively . |
| Approach: | They propose to annotate adjuncts marked by -de in Japanese and a second scheme to annnotate them in a more direct manner. |
| Outcome: | The proposed annotation schemes are more accurate than the first one. |
Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models (2025.acl-long)
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Zhisong Zhang, Yan Wang, Xinting Huang, Tianqing Fang, Hongming Zhang, Chenlong Deng, Shuaiyi Li, Dong Yu
| Challenge: | Large language models have demonstrated remarkable performance across a wide range of language tasks due to their remarkable ability in context modeling. |
| Approach: | They propose to use parallel context encoding to reduce attention entropy by incorporating attention sinks and selective mechanisms to reduce irregular attention . they also propose to incorporate attention sink mechanisms into the parallel encoded context to reduce the irregular attention. |
| Outcome: | The proposed methods lower irregular attention entropy and narrow performance gaps. |