Papers by Xingchao Liu
LanguageFlow: Advancing Diffusion Language Generation with Probabilistic Flows (2024.naacl-long)
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| Challenge: | Recent work has demonstrated success in controlling sentence attributes and structure based on diffusion language models. |
| Approach: | They propose a language-rectified flow method that reformulates standard probabilistic flow models to learn ordinary differential equations to transport between the source and target distributions. |
| Outcome: | The proposed method outperforms baselines on three fine-grained control tasks and multiple high-quality text editing tasks. |
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)
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| Challenge: | Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon. |
| Approach: | They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function. |
| Outcome: | The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks. |
Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models (2022.emnlp-main)
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| Challenge: | Xu et al., 2021) find that retrieval-reader models overfit top-rank passages and fail to reason over entire retrieval passages. |
| Approach: | They propose a passage mask mechanism which desensitizes the impact from top-rank retrieval passages and prevents the model from overfitting. |
| Outcome: | Experiments on open question answering, dialogue conversation, and fact verification show that the proposed model outperforms baselines. |