Papers by Mengyue Wu
A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models (2025.naacl-long)
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| Challenge: | Large Vision-Language Models (LVLMs) are hardly comprehensively evaluated for their cognitive abilities. |
| Approach: | They propose to evaluate high-level cognitive abilities of Large Vision-Language Models (LVLMs) using images with rich semantics. |
| Outcome: | The proposed evaluation benchmark consists of 251 images along with comprehensive annotations. |
Tracking Life’s Ups and Downs: Mining Life Events from Social Media Posts for Mental Health Analysis (2025.acl-long)
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| Challenge: | Existing studies have indicated that major life events can greatly impact individuals’ mental health, but shedding its light on social media data is challenging due to the complexity and ambiguity nature of life events. |
| Approach: | They propose to extract life events mentioned in posts on social media to uncover a social media event dataset which includes 12 major life event categories that are likely to occur in everyday life. |
| Outcome: | The proposed dataset includes 12 life event categories that are likely to occur in everyday life and is human-annotated under iterative procedure and boasts a high level of quality. |
Transcribing Vocal Communications of Domestic Shiba lnu Dogs (2023.findings-acl)
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| Challenge: | a recent study has focused on how animals communicate, but the study has been limited . previous studies have focused on a simple classification problem, requiring a model to get a label . |
| Approach: | They extract Shiba Inu dogs' vocal communications from YouTube videos and translate them into phonetic scripts using a systematic process. |
| Outcome: | The proposed framework produces the first-of-its-kind Shiba Inu vocal communication dataset . it will be useful for future research in zoology and linguistics. |
Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation (2023.emnlp-main)
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| Challenge: | Controlling chatbot utterance generation with multiple attributes is a useful but under-studied problem. |
| Approach: | They propose a framework that possesses strong controllability with a weighted decoding paradigm and improves generation quality with an attribute semantics space. |
| Outcome: | The proposed framework achieves high control accuracy with simultaneous control of 3 aspects while producing interesting and sensible responses even in an out-of-distribution robustness test. |
Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (2023.emnlp-main)
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| Challenge: | Existing mental disease detection methods are not backed by domain knowledge and thus fail to produce interpretable results. |
| Approach: | They propose a framework that can learn the shared clues of all diseases while also capturing the specificity of each single disease. |
| Outcome: | Experiments on the detection of 7 diseases show that the proposed model can boost detection performance by more than 10%, especially in relatively rare classes. |
A Diverse and Effective Retrieval-Based Debt Collection System with Expert Knowledge (2025.naacl-industry)
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| Challenge: | Existing debt collection systems lack script diversity, contextual relevance and coherence due to their complexity. |
| Approach: | They propose a script library based on real-world debt collection conversations and a retrieval based response system for contextual relevance. |
| Outcome: | The proposed system improves script diversity and responds to debtor-collector conversations better through knowledge distillation. |
Phonetic and Lexical Discovery of Canine Vocalization (2024.findings-emnlp)
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| Challenge: | Existing methods to study animal language systems rely on human prior knowledge on limited data. |
| Approach: | They propose a self-supervised approach that enables the accurate classification of phones and an adaptive grammar induction method that identifies phone sequence patterns that suggest a preliminary vocabulary within dog vocalizations. |
| Outcome: | The proposed approach breaks the barrier existing approaches relying on human prior knowledge on limited data. |
Toward Automatic Discovery of a Canine Phonetic Alphabet (2025.acl-long)
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| Challenge: | a new algorithm for vocalization communication between dogs is being developed . phonetic units alone are not sufficient to constitute a "language" |
| Approach: | They propose an algorithm that produces a complete alphabet of distinct canine phonemes . the algorithm is expected to function on canines and other animal species . |
| Outcome: | The proposed algorithm produces a complete alphabet of distinct canine phoneme-like units . it is expected to work on canines and other animal species . |
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue (2023.tacl-1)
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| Challenge: | Existing task-oriented dialogue systems lack ontology-aware pretraining methods for task-orientated dialogue. |
| Approach: | They propose an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) . they propose to pretrain on large-scale contextual text data to bridge the gap between the pretraining method and downstream tasks. |
| Outcome: | The proposed model achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks. |
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL (2021.findings-acl)
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| Challenge: | Recent work on Text-to-SQL for multi-turn dialogue has attracted great interest . current approaches mostly employ end-to end models and face data sparsity problems . |
| Approach: | They propose a decoupled multi-turn text-to-SQL framework where dialogue context is explicitly solved by an utterance rewrite model and a single-turn Text-toSQl parser are proposed. |
| Outcome: | The proposed method outperforms existing models on SParC and CoSQL datasets without annotated in-domain data. |
Automatic Reconstruction of Ancient Chinese Pronunciations (2024.findings-emnlp)
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| Challenge: | A human language is comprised of a pronunciation system and a writing system, both evolving and changing over time. |
| Approach: | They reformulate existing phonetic rules into a dataset of 70,943 entries for 17,001 Chinese characters and use it to perform a temporal prediction task. |
| Outcome: | The transformer-based model significantly advances the digitization and computational reconstruction of ancient Chinese phonology, providing a more complete and temporally contextualized resource for computational linguistics and historical research. |
Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)
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Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu
| Challenge: | Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules . |
| Approach: | They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain . |
| Outcome: | The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information . |
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat (2022.emnlp-main)
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| Challenge: | Existing human-machine dialogue systems are not able to provide diagnostic information for depression diagnosis due to stigma associated with mental illness. |
| Approach: | They propose to construct a Chinese Dialogue Dataset for depression-diagnosis-oriented chat based on clinical depression diagnostic criteria. |
| Outcome: | The proposed system can be used to diagnose depression using a Chinese Dialogue Dataset. |
MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics (2025.naacl-industry)
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| Challenge: | Large language models (LLMs) have been used in clinical decision support, medical education and patient communication. |
| Approach: | They propose a benchmark to evaluate large language models in the domain of medical ethics and assess their grasp of medical ethical principles and their application across diverse scenarios. |
| Outcome: | The proposed framework assesses the models’ grasp of medical ethics principles and their ability to apply them across diverse scenarios. |
Semi-Supervised Diseased Detection from Speech Dialogues with Multi-Level Data Modeling (2026.findings-acl)
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| Challenge: | Existing methods for learning from weakly-supervised speech data are hampered by severe data scarcity and the subjective nature of clinical annotations. |
| Approach: | They propose a framework that explicitly models pathological traits by jointly learning from frame-level, segment-level and session-level representations within unsegmented clinical dialogues. |
| Outcome: | The proposed framework is model-agnostic, robust across languages and conditions, and highly data-efficient. |
Mapping Long-term Causalities in Psychiatric Symptomatology and Life Events from Social Media (2024.naacl-long)
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Siyuan Chen, Meilin Wang, Minghao Lv, Zhiling Zhang, Juqianqian Juqianqian, Dejiyangla Dejiyangla, Yujia Peng, Kenny Zhu, Mengyue Wu
| Challenge: | Existing studies focus on the semantic content of social media posts, overlooking the evolving nature of mental disorders and symptoms. |
| Approach: | They extract causality between psychiatric symptoms and life events from social media posts and extract temporal attributes to improve diagnosis and treatment planning. |
| Outcome: | The extracted causality features improve diagnostic and treatment planning and improve performance in tasks such as depression and diagnosis point detection. |
Symptom Identification for Interpretable Detection of Multiple Mental Disorders on Social Media (2022.emnlp-main)
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| Challenge: | Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability due to lack of symptom modeling. |
| Approach: | They propose to annotate a social media corpus of symptom classes related to 7 mental disorders using a knowledge graph and a new annotation framework to facilitate further research. |
| Outcome: | The proposed model outperforms strong pure-text baselines and provides convincing MDD explanations with case studies. |