Papers by Yanyan Li

DNN-driven Gradual Machine Learning for Aspect-term Sentiment Analysis (2021.findings-acl)

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Murtadha Ahmed, Qun Chen, Yanyan Wang, Youcef Nafa, Zhanhuai Li, Tianyi Duan

Challenge:	Existing methods for Aspect-Term Sentiment Analysis (ATSA) use pre-specified lexicons to extract sentiment features.
Approach:	They propose a Deep Neural Network-driven approach for Aspect-Term Sentiment Analysis (ATSA) that leverages shared features between labeled and unlabeled instances for knowledge conveyance.
Outcome:	The proposed approach consistently achieves state-of-the-art performance on real benchmark data.

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)

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Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, Hui Xiong

Text Generation

Challenge:	Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables.
Approach:	They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input.
Outcome:	The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets.

Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence (2024.findings-acl)

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Weixiang Zhao, Zhuojun Li, Shilong Wang, Yang Wang, Yulin Hu, Yanyan Zhao, Chen Wei, Bing Qin

Language Models

Challenge:	Emotional Intelligence (EI) is a key concept in the field of human intelligence.
Approach:	They propose a method to enhance EI of large language models by naive fine-tuning on EI-related tasks.
Outcome:	The proposed method improves EI of two LLM-based assistants without compromising GI.

Summarizing Dialogues with Negative Cues (2022.coling-1)

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Junpeng Liu, Yanyan Zou, Yuxuan Xi, Shengjie Li, Mian Ma, Zhuoye Ding

Dialogue Systems & Conversational Agents

Challenge:	Abstractive dialogue summarization aims to convert long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored.
Approach:	They propose to have the model perceive the redundant parts of an input dialogue history during the training phase.
Outcome:	The proposed method significantly outperforms baselines on the semantic matching and factual consistent based metrics.

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)

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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che

Language Models

Challenge:	Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits.
Approach:	They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task.
Outcome:	The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists.

ENPMR-Bench: Benchmarking Proactive Memory Retrieval for Emotional Support Agents (2026.findings-acl)

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Xing Fu, Yulin Hu, Mengtong Ji, Haozhen Li, Yixin Sun, Weixiang Zhao, Yanyan Zhao, Bing Qin

Information Retrieval

Challenge:	Existing research treats memory as a mechanism for factual retention, neglecting its role in shaping users’ emotional experiences.
Approach:	They propose a benchmark for evaluating Emotional Need-aware Proactive Memory Retrieval (ENPMR) it enables agents to infer users’ latent emotional needs and proactively retrieve appropriate memories to support empathetic interaction.
Outcome:	The proposed benchmark includes over 1,800 memory-augmented dialogues and defines structured mappings between emotional needs and supportive memory types.

Look Beyond Feeling: Unveiling Latent Needs from Implicit Expressions for Proactive Emotional Support (2025.emnlp-main)

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Xing Fu, Haozhen Li, Bichen Wang, Hao Yang, Yanyan Zhao, Bing Qin

Dialogue Systems & Conversational Agents

Challenge:	Large language models (LLMs) are gaining popularity as scalable tools for mental health support . however, nearly half of individuals do not receive timely support due to limited selfawareness or reluctance to seek help.
Approach:	They propose a proactive emotional support framework that leverages principles of active listening to uncover implicit user needs.
Outcome:	The proposed model elicits implicit emotional needs and delivers empathetic support compared to baselines .

Supervised Gradual Machine Learning for Aspect-Term Sentiment Analysis (2023.tacl-1)

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Yanyan Wang, Qun Chen, Murtadha H.M. Ahmed, Zhaoqiang Chen, Jing Su, Wei Pan, Zhanhuai Li

Sentiment Analysis

Challenge:	Recent work shows that Aspect-Term Sentiment Analysis (ATSA) can be performed by Gradual Machine Learning (GML) but the current unsupervised solution is limited by inaccurate knowledge conveyance.
Approach:	They propose a supervised approach which leverages binary polarity relations between instances to enable supervised knowledge conveyance.
Outcome:	The proposed approach outperforms pure DNN solutions on real benchmark data.

Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity (2022.emnlp-main)

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Jiahao Li, Quan Wang, Zhendong Mao, Junbo Guo, Yanyan Yang, Yongdong Zhang

Syntactic Text Processing

Challenge:	Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.
Approach:	They propose an auxiliary task of Chinese pronunciation prediction to improve CSC . they propose adaptive weighting schemes and a delicate correction strategy .
Outcome:	The proposed auxiliary task improves Chinese pronunciation prediction on three benchmarks.

Ro-SLM: Onboard Small Language Models for Robot Task Planning and Operation Code Generation (2026.findings-acl)

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Wenhao Wang, Yanyan Li, Long Jiao, Jiawei Yuan

Language Models

Challenge:	Recent advances in large language models (LLMs) provide robots with contextual reasoning abilities to comprehend human instructions.
Approach:	They propose a framework that enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning.
Outcome:	The proposed framework enables reliable SLM-driven robot operation by distilling LLMs’ knowledge and reasoning.

Self-Foveate: Enhancing Diversity and Difficulty of Synthesized Instructions from Unsupervised Text via Multi-Level Foveation (2025.findings-acl)

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Mingzhe Li, Xin Lu, Yanyan Zhao

Responsible & Trustworthy NLP

Challenge:	Existing methods for training large language models rely on human effort for data annotation.
Approach:	They propose an unsupervised method that generates unsupervised instruction from unsupervised text using a "Micro-Scatter-Macro" method that excavates fine-grained information embedded in unsupervised texts.
Outcome:	The proposed method improves diversity and difficulty of synthesized instructions across multiple unsupervised corpora and diverse model architectures.