Papers by Yuchen Zhai
UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings (2021.findings-emnlp)
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| Challenge: | Existing methods to reduce word embedding parameters ignore semantic information . existing methods do not consider semantic information, allowing for performance degradation . |
| Approach: | They propose a method that leverages semantic similarity with weight sharing to reduce dimensionality of word embeddings. |
| Outcome: | The proposed method reduces word embedding parameters by more than 11x on a standard English-German dataset. |
Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition (2020.emnlp-main)
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| Challenge: | Using labeled data, named entity recognition is labor-intensive, time-consuming and expensive. |
| Approach: | They propose a method which decomposes named entity into two parts: entity and context. |
| Outcome: | The proposed method improves the generalization ability of models under limited observational examples. |
Investigating Hallucinations in Simultaneous Machine Translation: Knowledge Distillation Solution and Components Analysis (2025.naacl-long)
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| Challenge: | Existing methods to mitigate hallucinations in siMT generate fluency but unfaithful translation. |
| Approach: | They propose a method that utilizes the OMT model to mitigate hallucinations in SiMT. |
| Outcome: | The proposed method reduces hallucinations and improves the SiMT performance. |
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets (2026.findings-acl)
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Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen
| Challenge: | Existing methods for dataset poisoning require full-dataset poison, which breaks code compilability. |
| Approach: | They propose a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. |
| Outcome: | The proposed method contaminates 10% of the dataset while maintaining 100% compilability and functional correctness. |
Continual Few-shot Intent Detection (2022.coling-1)
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| Challenge: | Existing intent detection systems are trained with lots of labeled data over a predefined set of intent classes. |
| Approach: | They propose a prefix-guided lightweight encoder with three auxiliary strategies to prevent catastrophic forgetting and negative knowledge transfer across tasks. |
| Outcome: | The proposed system prevents catastrophic forgetting and encourages positive knowledge transfer across tasks. |