Papers by Xiaohan Ma
Multi-Channel Spatio-Temporal Transformer for Sign Language Production (2024.lrec-main)
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| Challenge: | Sign language production models ignore structural correlations between channels and use multi-channel spatial attention to capture correlations across channels. |
| Approach: | They propose a novel approach to transform sign language into a unified feature representation using multi-channel spatial attention and temporal attention to learn sequential dependencies for each channel over time. |
| Outcome: | The proposed model outperforms state-of-the-art models on two sign language datasets from diverse cultures. |
FFAEval: Evaluating Dialogue System via Free-For-All Ranking (2023.findings-emnlp)
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| Challenge: | Existing evaluation metrics for open-domain dialogue systems show poor correlation with human assessment. |
| Approach: | They propose a free-for-all human evaluation framework that shares dialogue history with annotators for multi-turn scoring. |
| Outcome: | The proposed framework achieves a strong correlation with human assessment on English and Chinese dialogue systems. |
Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
| Approach: | They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them. |
| Outcome: | The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods. |
Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)
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| Challenge: | Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process. |
| Approach: | They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input. |
| Outcome: | The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT. |