🧑‍🔬 Fanghua Ye

🏛️ Affiliations

🎓University College LondonLondon · United Kingdom2019–2025 · 36 papers🎓Sun Yat-sen UniversityGuangzhou · China2017–2020 · 20 papers🏢TencentShenzhen · China2024–2026 · 18 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation (2022 · 131 citations)
   Dialogue state tracking research relies heavily on MultiWOZ, but even its corrected releases (2.1–2.3) still have many wrong or inconsistent state labels.
2. Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models (2025 · 65 citations)
   The paper asks whether the six classic obstacles for neural MT identified by Koehn and Knowles (2017) still apply when translation is done by decoder-only LLMs.
3. Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models (2026 · 6 citations)
   Standard LLM leaderboards reward textual skill and helpfulness, but they rarely check whether a model actually reads people well.
4. Too Good to be Bad: On the Failure of LLMs to Role-Play Villains (2026 · 3 citations)
   LLMs are increasingly used for interactive fiction, games and co-written stories, yet nobody had systematically checked whether they can convincingly play antagonists.
5. Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding (2026 · 1 citation)
   When LLMs answer questions or summarize from a supplied passage, they often fall back on knowledge memorized in pretraining.
6. BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026)
   Current LLM-based text-to-speech systems mostly use the LLM as a backbone for generating speech tokens. Controllable synthesis then depends on costly, inconsistent human annotation of speech with style labels, and the model's instruction-following ability goes largely unused. Inspired by operationalism, the authors…
7. Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026)
   LLMs are increasingly used in consequential settings, but little is known about the values they apply when distributing scarce resources.

🤝 Co-authors here

12 of 29 co-authors with a page, the most papers together first.

• Ruotian Ma ×4
• Xingyu Chen ×4
• Qingxuan Jiang ×3
• Qu Yang ×3
• Yue Wang ×3
• Zhaopeng Tu ×3
• Jian Li ×2
• Mengru Wang ×2
• Xiaolong Li ×2
• Zhengliang Shi ×2
• Bang Zhang
• Derek F. Wong

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Findings of the Association for Computational Linguistics ×4: Findings of the Association for Computational Linguistics 2026 ×4
• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
• Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL): SIGDIAL 2022
• Transactions of the Association for Computational Linguistics (TACL): Transactions of the Association for Computational Linguistics (TACL) 2025

🔗 Cited by

Papers of our library cite their works 110 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated 1 November 2026

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