Papers by Ziwei Ma
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
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Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
| Approach: | They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications. |
| Outcome: | The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models . |
SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes (2025.findings-emnlp)
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Iva Bojic, Qi Chwen Ong, Stephanie Hilary Xinyi Ma, Lin Ai, Zheng Liu, Ziwei Gong, Julia Hirschberg, Andy Hau Yan Ho, Andy W. H. Khong
| Challenge: | SMARTMiner extracts specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes. |
| Approach: | They propose a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes. |
| Outcome: | The framework extracts behavior change goal spans and categorizes their SMARTness. |
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction. |
| Approach: | This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies . |
| Outcome: | The survey examines the effectiveness of MERC and its evaluation strategies. |
MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluation (2025.acl-long)
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| Challenge: | Existing evaluation methods focus on single-language scenarios, overlooking multilingual and cross-lingual contexts. |
| Approach: | They propose a tool to assess instruction-following capabilities across 23 different languages with 1667 verifiable instruction tasks. |
| Outcome: | MaXIFE evaluates instruction-following capabilities across 23 languages with 1667 verifiable instruction tasks. |
MessToClean: Evidence-Grounded Structure-Preserving Reconstruction for Real-World Degraded Exam Paper Images (2026.acl-long)
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Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao
| Challenge: | Existing Multimodal Large Language Models (MLLMs) fail under RDEI, leading to disrupted structure and evidence-unsupported hallucinations. |
| Approach: | They propose a backbone-agnostic, evidence-driven pipeline that treats off-the-shelf MLLMs as interchangeable components to improve stem consistency and figure consistency. |
| Outcome: | The proposed pipeline improves stem consistency by 1.01-3.18%, figure consistency by 0.50-49.16%, and refusal F1 by 1.06-10.88% across question types. |