Papers by Chun Gan
Dependency Parsing as MRC-based Span-Span Prediction (2022.acl-long)
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| Challenge: | Existing methods for dependency parsing address the issue that edges should be constructed at the text span/subtree level rather than word level. |
| Approach: | They propose a method that constructs dependency trees by directly modeling span-span relations by modeling subtree-subtree relationships. |
| Outcome: | The proposed method constructs dependency trees by modeling span-span relations . it can retrieve missing spans in the span proposal stage, which leads to higher recall . |
Probabilistic Graph Reasoning for Natural Proof Generation (2021.findings-acl)
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| Challenge: | Existing approaches to reasoning over formal representations do not explicitly consider inter-dependency between answers and proofs. |
| Approach: | They propose a novel approach for joint answer prediction and proof generation using an induced graphical model. |
| Outcome: | The proposed approach achieves 10%-30% improvement on QA accuracy in evaluations under diverse conditions. |
Vocabulary Learning via Optimal Transport for Neural Machine Translation (2021.acl-long)
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| Challenge: | Empirical results show that VOLT beats widely-used vocabularies in diverse scenarios, including WMT-14 English-German translation, TED bilingual translation, and TED multilingual translation. |
| Approach: | They propose a token dictionary solution that can be used without trial training to find the best dictionary with a proper size. |
| Outcome: | The proposed solution beats widely-used vocabularies in English-German translation, TED bilingual translation, and TED multilingual translation. |
Triggerless Backdoor Attack for NLP Tasks with Clean Labels (2022.naacl-main)
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Leilei Gan, Jiwei Li, Tianwei Zhang, Xiaoya Li, Yuxian Meng, Fei Wu, Yi Yang, Shangwei Guo, Chun Fan
| Challenge: | Backdoor attacks are a new threat to neural natural language processing models due to the fragility and lack of interpretability of NLP models. |
| Approach: | They propose a method to perform backdoor attacks without an external trigger . they propose to use clean-labeled examples to generate poisoned clean-labelled examples . |
| Outcome: | The proposed strategy is effective and hard to defend due to its triggerless nature. |