Papers by Weiguo Liu
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup (2021.emnlp-main)
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| Challenge: | Experiments on five text classification benchmarks and five backbone models have shown that our methods reduce the error rate over Mixup variants in a significant margin (up to 31.3%), especially in low-resource conditions (upto 17.5%). |
| Approach: | They propose to add a small adversarial perturbation to the mixing coefficients rather than the examples to relax locally linear constraints. |
| Outcome: | Experiments on five text classification benchmarks and five backbone models show that the proposed methods reduce the error rate over Mixup variants by 31.3%, especially in low-resource conditions. |
Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance (2025.naacl-long)
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| Challenge: | Large language models (LLMs) provide superior summarization quality, but their high computational resource requirements limit practical use applications. |
| Approach: | They evaluate 19 small language models for news summarization across 2,000 news samples . they find that top-performing models achieve comparable results to those of 70B LLMs . |
| Outcome: | The proposed models achieve comparable results to 70B LLMs while generating more concise summaries. |
DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (2021.findings-emnlp)
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| Challenge: | Existing studies focus on the self and inter-personal dependencies in multi-modal conversations, but they ignore the temporal and spatial dependencies. |
| Approach: | They propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics. |
| Outcome: | The proposed models outperform the state-of-the-art on three benchmark datasets. |