| 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. |
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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. |
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. |
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. |
Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing (D19-1)
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| Challenge: | Existing approaches to learn cross-lingual word embeddings in a contextual space are lacking. |
| Approach: | They propose a method to generate cross-lingual contextualized word embeddings using pre-trained BERT models by learning a linear transformation from contextual word alignments. |
| Outcome: | The proposed approach outperforms state-of-the-art models on zero-shot cross-lingual transfer parsing and is highly competitive with existing models. |
Zero-shot Dependency Parsing with Pre-trained Multilingual Sentence Representations (D19-61)
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| Challenge: | Pretrained sentence representations have set the new state of the art in many language understanding tasks. |
| Approach: | They propose to use a multilingual corpus to train deep bidirectional sentence representations that are fully lexicalized to allow for the development of an unsupervised universal dependency parser. |
| Outcome: | The proposed approach outperforms the best CoNLL 2018 systems in all of the shared task’s six truly low-resource languages while using a single system. |
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
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| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
| Approach: | They propose a task that uses multilingual BERT representations of both the mention and context as input and explore zero-shot language transfer. |
| Outcome: | The proposed model performs well in both monolingual and multilingual settings. |
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. |
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. |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
| Outcome: | The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks. |
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 . |