Papers by Ting Chen

AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)

Challenge:	Existing methods fine-tune pre-trained models on cognitive data, ignoring the semantic gap between texts and cognitive signals.
Approach:	They propose a framework that can induce fine-grained cognitive features from cognitive data and incorporate them into pre-trained language models by adaptively adjusting the weight of cognitive features for different NLP tasks.
Outcome:	The proposed framework can induce fine-grained cognitive features from cognitive data and incorporate them into BERT by adaptively adjusting weight of cognitive features for different NLP tasks.

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Nuwa Xi, Yuhan Chen, Sendong Zhao, Haochun Wang, GongZhang GongZhang, Bing Qin, Ting Liu

Improving Image Captioning via Predicting Structured Concepts (2023.emnlp-main)

Challenge:	Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data.
Approach:	They propose a new training paradigm which exploits the inherent information in CoT for iterative generation.
Outcome:	The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself.

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Ting Wang, Weidong Chen, Yuanhe Tian, Yan Song, Zhendong Mao

Multimodality

Challenge:	Existing studies on image captioning ignore the relationship between concepts . current methods for image caption generation ignore this relationship .
Approach:	They propose a structured concept predictor to predict concepts and their structures . they integrate these predictions into captioning to enhance visual signals .
Outcome:	The proposed approach improves image captioning performance by using semantic concepts as a bridge between images and texts.

Bridging the Memorization-Utilization Gap: Near-Lossless Context Compression via Reinforcement Learning (2026.acl-long)

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Yujan Ting, Xu Tang, Terrence Chen, Weijing Huang

TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2025.acl-long)

Challenge:	Recent advances in context compression have failed to effectively utilize compressed representations for downstream tasks.
Approach:	They propose a holistic training paradigm that uses outcome-based RL to enable implicit expansion.
Outcome:	The proposed model outperforms previous models on NIAH, LongBench and multi-hop reasoning.

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Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen

Green & Sustainable NLP

Challenge:	Existing methods for product attribute value identification face critical challenges . seller-provided attribute values are often incomplete or inaccurate .
Approach:	They propose a retrieval-based method that uses taxonomy-aware contrastive learning . they use product profiles and candidate values to encode and retrieve attributes based on similarity .
Outcome:	The proposed method is based on a taxonomy-aware, hard negative sampling and adaptive inference with dynamic thresholds.

Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)

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Yutai Hou, Yongkui Lai, Cheng Chen, Wanxiang Che, Ting Liu

Semantic Text Processing

Challenge:	Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks .
Approach:	They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain.
Outcome:	The proposed model outperforms baseline models on two public datasets on intent and slot . the proposed model significantly outperformed baseline models in one and five shots settings.

CXR Data Annotation and Classification with Pre-trained Language Models (2022.coling-1)

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Nina Zhou, Ai Ti Aw, Zhuo Han Liu, Cher heng Tan, Yonghan Ting, Wen Xiang Chen, Jordan sim zheng Ting

Enhancing Dialogue Symptom Diagnosis with Global Attention and Symptom Graph (D19-1)

Challenge:	Existing tools for clinical data annotation are limited to specific institutions due to differences in writing style, structure, language use and label definition.
Approach:	They propose a weak supervision annotation framework with two improvements over existing ones . the framework provides an efficient form of sample selection and data auto-annotation .
Outcome:	The proposed framework provides better results for clinical data annotation tasks compared to existing frameworks.

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Xinzhu Lin, Xiahui He, Qin Chen, Huaixiao Tou, Zhongyu Wei, Ting Chen

Natural Language Interfaces

Challenge:	Existing studies on symptom diagnosis based on EHRs focus on the standard electronic medical records, but the dialogues between doctors and patients that contain more rich information are not well studied.
Approach:	They propose to build a global attention mechanism to capture more symptom related information and build symptom graphs to model the associations between symptoms rather than treating each symptom independently.
Outcome:	The proposed model achieves the state-of-the-art on the constructed dataset.

From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)

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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin

LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training (2022.findings-emnlp)

Challenge:	rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research.
Approach:	They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication.
Outcome:	The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication.

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Qi Shi, Qian Liu, Bei Chen, Yu Zhang, Ting Liu, Jian-Guang Lou

SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework (2026.acl-long)

Challenge:	Existing approaches to language-based environment manipulation are difficult to generalize across environments.
Approach:	They propose a general framework for language-based environment manipulation tasks that can deal with various environments using the same generative language model.
Outcome:	The proposed framework achieves new state-of-the-art results on four of the tasks and the execution-guided pre-training strategy brings remarkable improvements on all experimental tasks.

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Chengran Yang, Ting Zhang, Jinfeng Jiang, Xin Zhou, Haoye Tian, Mingzhe Du, Jieke Shi, Junkai Chen, Yikun Li, Eng Lieh Ouh, Lwin Khin Shar, David Lo

Dataset for the First Evaluation on Chinese Machine Reading Comprehension (L18-1)

Challenge:	Existing methods for automating vulnerability repair suffer from syntactic overfitting . nvd published 49,230 Common Vulnerabilities and Exposures (CVE) records in 2025 alone .
Approach:	They propose a semantic-aware reward framework that optimizes for code semantic equivalence rather than lexical mimicry.
Outcome:	The proposed framework outperforms state-of-the-art frameworks on repository-level splits . it incorporates expert-aligned reasoning mechanism that grounds patch generation in structured diagnosis.

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Yiming Cui, Ting Liu, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

A Sentence Cloze Dataset for Chinese Machine Reading Comprehension (2020.coling-main)

Challenge:	Existing reading comprehension datasets are mostly in English .
Approach:	They propose a Chinese reading comprehension dataset to add diversity to existing reading comprehension data . proposed dataset contains cloze-style reading comprehension and user query reading comprehension .
Outcome:	The proposed dataset is based on a Chinese reading comprehension dataset . it includes two types of cloze-style and user query reading comprehension . the proposed dataset hosted the 1st Evaluation on Chinese Machine Reading Comprehension (CMRC-2017)

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Yiming Cui, Ting Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu

TextBrewer: An Open-Source Knowledge Distillation Toolkit for Natural Language Processing (2020.acl-demos)

Challenge:	Using cloze-style reading comprehension, Chinese machine reading comprehension datasets are becoming more and more popular . a new task is proposed to fill the right candidate sentence into the passage with several blanks .
Approach:	They propose a Chinese task to fill the right candidate sentence into a passage with blanks . they build a dataset to evaluate the difficulty of the task and make fake candidates .
Outcome:	The proposed task fills the right candidate sentence into the passage with blanks . the proposed dataset contains over 100K blanks within over 10K passages based on Chinese narrative stories .

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Ziqing Yang, Yiming Cui, Zhipeng Chen, Wanxiang Che, Ting Liu, Shijin Wang, Guoping Hu

FAER: Benchmarking VLMs for Failure-Aware Embodied Reasoning (2026.findings-acl)

Challenge:	Large pre-trained language models have hundreds of millions of parameters and take several gigabytes of memory to train and inference.
Approach:	They propose an open-source knowledge distillation toolkit designed for natural language processing that provides a set of predefined distillation methods and can be extended with custom code.
Outcome:	The proposed method is comparable with or even higher than the public distilled BERT models with similar numbers of parameters.

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Hao Song, Kaifeng Liu, Yuanxing Liu, Xiang Tian, Xuesong Wang, Chen Yifan, Weinan Zhang, Ting Liu

Task-oriented Dialogue System for Automatic Diagnosis (P18-2)

Challenge:	Visual-language models (VLMs) are the core component of embodied agents in perceiving the environment and making decisions.
Approach:	They propose a failure-aware benchmark to evaluate the performance of visual language models (VLMs) in long-horizon tasks.
Outcome:	The proposed benchmark evaluates the performance of 16 widely utilized VLMs and 4 LLMs for FAER tasks.

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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai

Natural Language Interfaces

Challenge:	Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another.
Approach:	They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports.
Outcome:	The proposed system can collect additional symptoms from conversation and improve disease identification accuracy.

Shadow-Activated Backdoor Attacks on Multimodal Large Language Models (2025.findings-acl)

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Ziyi Yin, Muchao Ye, Yuanpu Cao, Jiaqi Wang, Aofei Chang, Han Liu, Jinghui Chen, Ting Wang, Fenglong Ma

Robustness in NLP

Challenge:	Existing backdoor attacks on Multimodal Large Language Models are less applicable to open-ended conversations with users.
Approach:	They propose a shadow-activated backdoor attack scenario where attackers inject malicious content into the responses of MLLMs when the responses explicitly relate to the shadowed object.
Outcome:	The proposed framework achieves the desired behaviors by constructing a poisoned dataset and implementing an attention-regularized tuning strategy.

A Span-Extraction Dataset for Chinese Machine Reading Comprehension (D19-1)

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Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu

Machine Reading Comprehension

Challenge:	Existing reading comprehension datasets are mostly in English . MRC is a new field of research that aims to comprehend the context of articles and answer the questions based on them.
Approach:	They propose a Span-Extraction dataset for Chinese machine reading comprehension to add language diversities to existing reading comprehension datasets.
Outcome:	The proposed dataset is composed of 20,000 real questions annotated on Wikipedia paragraphs by human experts.

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)

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Ting Xu, Huiyun Yang, Zhen Wu, Jiaze Chen, Fei Zhao, Xinyu Dai

Sentiment Analysis

Challenge:	Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area.
Approach:	They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews.
Outcome:	The proposed dataset is manually annotated to better fit real-world scenarios.

PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning (2024.naacl-long)

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Tianrong Zhang, Zhaohan Xi, Ting Wang, Prasenjit Mitra, Jinghui Chen

FastSeq: Make Sequence Generation Faster (2021.acl-demo)

Challenge:	Existing studies have shown that pre-trained language models can be backdoored such that model behavior is manipulated when trigger tokens are presented.
Approach:	They propose a backdoor mitigation strategy for NLP models via adversarial prompt-tuning in few-shot settings that uses two extra sets of soft tokens which approximate the trigger and counteract it respectively.
Outcome:	The proposed method keeps model parameters intact and only utilizes two extra sets of soft tokens which approximate the trigger and counteract it respectively.

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Yu Yan, Fei Hu, Jiusheng Chen, Nikhil Bhendawade, Ting Ye, Yeyun Gong, Nan Duan, Desheng Cui, Bingyu Chi, Ruofei Zhang

Multi-Lingual Question Generation with Language Agnostic Language Model (2021.findings-acl)

Challenge:	Transformer-based models have made tremendous impact in natural language generation, but inference speed is still a bottleneck due to large model size and intensive computing involved in auto-regressive decoding process.
Approach:	They propose an attention cache optimization, an efficient algorithm for detecting repeated n-grams, and an asynchronous generation pipeline with parallel I/O to accelerate sequence generation without loss of accuracy.
Outcome:	The proposed framework can accelerate the sequence generation by 4x to 9x with a simple one-line code change for a set of widely used and diverse models.

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Bingning Wang, Ting Yao, Weipeng Chen, Jingfang Xu, Xiaochuan Wang

ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)

Challenge:	Existing training data for question generation in English and Chinese is limited . a language-agnostic model is developed to learn the shared representation from several languages in a single architecture.
Approach:	They propose a language-agnostic language model which learns the shared representation from several languages in a single architecture.
Outcome:	The proposed model improves multi-lingual question generation over five languages.

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Ziyi Yin, Yuanpu Cao, Ting Wang, Jinghui Chen, Fenglong Ma

Training Medical QA Models Based on Mixed Rewards from Multiple-Choice and Open-Ended Questions (2025.findings-emnlp)

Challenge:	Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice.
Approach:	They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment.
Outcome:	The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent.

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Yue Qiu, Yujan Ting, Pei Dong, Terrence Chen, Weijing Huang

Question Answering

Challenge:	Reinforcement learning (RL) for large language models typically requires clear reward signals, which are often unavailable for open-ended (OE) questions where answer evaluation is ambiguous without scalable expert labeling.
Approach:	They propose a mixed-data approach to training large language models with varying reward clarity . they combine Multiple-choice questions (MCQs) with OE questions for which they use simpler, potentially noisy rewards such as Jaccard similarity or LLM-based evaluators.
Outcome:	The mixed-data approach improves medical question-answering performance across model scales.

MURAL: Multimodal, Multitask Representations Across Languages (2021.findings-emnlp)

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Aashi Jain, Mandy Guo, Krishna Srinivasan, Ting Chen, Sneha Kudugunta, Chao Jia, Yinfei Yang, Jason Baldridge

Semantic Text Processing

Challenge:	Image-caption pairs and translation pairs provide the means to learn deep representations of and connections between languages.
Approach:	They propose a dual encoder that integrates image-text matching and translation pairs to solve two tasks by learning from billions of pairs.
Outcome:	The proposed encoder outperforms ALIGN's cross-modal retrieval performance on well-resourced languages and significantly improves on under-resource languages.

Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020.emnlp-main)

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Sanyuan Chen, Yutai Hou, Yiming Cui, Wanxiang Che, Ting Liu, Xiangzhan Yu

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)

Challenge:	Existing methods to fine-tune deep pretrained language models face catastrophic forgetting problems.
Approach:	They propose a recall and learn mechanism which integrates pretraining and downstream tasks into a single mechanism.
Outcome:	The proposed method achieves state-of-the-art performance on the GLUE benchmark and better average performance than directly fine-tuning of BERT-large.

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Hieu Tran, Junda Wang, Yujan Ting, Hong Yu, Weijing Huang, Terrence Chen

Ethical NLP

Challenge:	Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare.
Approach:	They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components .
Outcome:	Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores.

Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024.acl-long)

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Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, Ting Liu