Papers by Xiang Huang

Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)

Challenge:	Existing methods to improve instruction tuning for large language models may cause catastrophic forgetting (CF) CF is a problem where previously learned abilities are degraded .
Approach:	They propose a continual instruction tuning method that uses key-part information gain to replay data and refine training objective.
Outcome:	The proposed method achieves superior performance on both seen and held-out tasks.

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Feiyang Wu, Peixin Huang, Yanli Hu, Zhen Tan, Xiang Zhao

AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)

Challenge:	Existing methods to model event associations struggle with semantic ambiguity and embedding bias.
Approach:	They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model .
Outcome:	The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism.

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Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, Jun Huang

Information Retrieval

Challenge:	Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information.
Approach:	They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods .
Outcome:	The proposed method improves stock trend prediction and financial question answering tasks.

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)

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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang

Low-Resource NLP

Challenge:	Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios.
Approach:	They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers .
Outcome:	The proposed model outperforms baselines and class transfer models in low-resource scenarios.

Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (2022.findings-naacl)

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Xiang Chen, Ningyu Zhang, Lei Li, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

Challenge:	Existing approaches for named entity recognition and relation extraction suffer from error sensitivity when irrelevant object images are incorporated in texts.
Approach:	They propose a hierarchical visual prefix fusion NeTwork for visual-enhanced entity and relation extraction using pluggable visual prefixed visual features.
Outcome:	The proposed method achieves state-of-the-art on three benchmark datasets.

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

Ciron: a New Benchmark Dataset for Chinese Irony Detection (2020.lrec-1)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Rong Xiang, Xuefeng Gao, Yunfei Long, Anran Li, Emmanuele Chersoni, Qin Lu, Chu-Ren Huang

Sentiment Analysis

Challenge:	Automatic Chinese irony detection often lacks labeled benchmark datasets . despite its pervasive nature, irony is a trope whose actual meaning differs from what is literally enunciated.
Approach:	They propose to use a Chinese benchmark dataset for automatic Chinese irony detection to provide a benchmark for machine learning models.
Outcome:	The proposed dataset includes more than 8.7K posts, collected from Weibo, a micro blogging platform.

Automatic Learning of Modality Exclusivity Norms with Crosslingual Word Embeddings (2020.starsem-1)

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Emmanuele Chersoni, Rong Xiang, Qin Lu, Chu-Ren Huang

Representation Learning

Challenge:	Normative studies on modality for English words are relatively common . however, they are limited to a relatively small number of languages and require costly ratings.
Approach:	They aim to learn a mapping between word embeddings and modality norms by training on a high-resource language and testing on . monolingual and crosslingual word embeds are used to predict modality association scores .
Outcome:	The proposed model predicts modality associations even when trained on an English resource and tested on a completely unseen language.

MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)

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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang

Multilinguality

Challenge:	Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool.
Approach:	They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images .
Outcome:	The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images.

Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)

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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang

ToM-Synth: Scaling Robust Theory of Mind in LLMs via 6,912 Structured Social Units (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables.
Approach:	They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments.
Outcome:	The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods.

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Guiyang Hou, Xiang Huang, Shangke Lyu, Yuchuan Wu, Weiyao Luo, Xinyu Mei, Yongliang Shen, Weiming Lu, Yongbin Li

Robustness in NLP

Challenge:	Existing methods endowing LLMs with Theory of Mind fail to internalize the augmented ToM into the LLM.
Approach:	They propose a factorial combinatorial synthesis framework that enables systematic synthesis of ToM data and uses it for RL fine-tuning.
Outcome:	The proposed framework yields a training dataset of 27,648 instances.

Contrastive Document Representation Learning with Graph Attention Networks (2021.findings-emnlp)

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Peng Xu, Xinchi Chen, Xiaofei Ma, Zhiheng Huang, Bing Xiang

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models (2026.findings-acl)

Challenge:	Existing methods for document representation learning are significantly affected by the scarcity of document-level data.
Approach:	They propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings.
Outcome:	Empirically, the proposed approach is effective in document classification and document retrieval tasks.

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Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, Xiang Li

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	Existing training paradigms rely on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process.
Approach:	They propose a framework that integrates large reasoning models with retrieval-augmented generation to improve reasoning fidelity and verification rigor.
Outcome:	Experiments on multiple benchmarks demonstrate the effectiveness of the proposed framework.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

RMTBench: Benchmarking LLMs Through Multi-Turn User-Centric Role-Playing (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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Hao Xiang, Tianyi Tang, Yang Su, Bowen Yu, An Yang, Fei Huang, Yichang Zhang, Yaojie Lu, Hongyu Lin, Xianpei Han, Jingren Zhou, Junyang Lin, Le Sun

Dialogue Systems & Conversational Agents

Challenge:	Existing benchmarks focus on character-centric approach and fail to reflect real-world applications.
Approach:	RMTBench is a user-centric bilingual role-playing benchmark featuring 80 diverse characters and over 8,000 dialogue rounds.
Outcome:	RMTBench features 80 diverse characters and over 8,000 dialogue rounds.

NICE: Neural Image Commenting with Empathy (2021.findings-emnlp)

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Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao

Visual Data in NLP

Challenge:	Emotion and empathy are examples of human qualities lacking in many human-machine interactions.
Approach:	They propose to generate images with human-generated comments with enhanced emotion and empathy while minimizing inappropriate or offensive outputs.
Outcome:	The proposed model generates more human-like and engaging image comments on two images with human-generated comments and human annotations while minimizing inappropriate or offensive outputs.

Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)

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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li

Challenge:	Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences.
Approach:	They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks.
Outcome:	The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge.

CLEEK: A Chinese Long-text Corpus for Entity Linking (2020.lrec-1)

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Weixin Zeng, Xiang Zhao, Jiuyang Tang, Zhen Tan, Xuqian Huang

Knowledge Representation

Challenge:	Entity linking is a fundamental task in natural language processing, says nigel kilgstrom . existing corpora for entity linking in china are lacking and deficient, he says . kilsmstrom: a new method for entity disambiguation can be developed for Chinese .
Approach:	They build a Chinese corpus of multi-domain long text for entity linking . they evaluate the difficulty of documents with respect to entity linking using a measure .
Outcome:	The proposed corpus is based on 100 documents from diverse domains and is publicly accessible.

TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition (2020.acl-main)

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Bill Yuchen Lin, Dong-Ho Lee, Ming Shen, Ryan Moreno, Xiao Huang, Prashant Shiralkar, Xiang Ren

MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)

Challenge:	Named entity recognition (NER) is a fundamental information extraction task that focuses on extracting entities from a given text and classifying them using pre-defined categories.
Approach:	They propose to use “entity triggers” to facilitate label-efficient learning of NER models.
Outcome:	The proposed model is significantly more cost-effective than the traditional neural NER frameworks.

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Shuaijie She, Wei Zou, Shujian Huang, Wenhao Zhu, Xiang Liu, Xiang Geng, Jiajun Chen

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)

Challenge:	Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data .
Approach:	They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language.
Outcome:	The proposed framework improves multilingual reasoning across languages on three benchmarks.

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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin

LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning (2026.acl-long)

Challenge:	Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination.
Approach:	They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns.
Outcome:	The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages.

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Zerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei, Linfeng Gao, Xiao Huang, Zhihong Zhang, Jinsong Su

WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild? (2025.emnlp-main)

Challenge:	Graph-based Retrieval-Augmented Generation (GraphRAG) is a new approach to document retrieval, but it is not suitable for legal reasoning.
Approach:	They propose a framework for reliable legal reasoning that structures knowledge as relational graphs and uses a multi-agent system to verify validity.
Outcome:	The proposed framework outperforms existing GraphRAG models in accurate and trustworthy legal analysis.

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An-Lan Wang, Jingqun Tang, Lei Liao, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Hao Liu, Yuliang Liu, Xiang Bai, Can Huang

Robustness in NLP

Challenge:	Existing benchmarks for document understanding in the wild are based on scanned or digital documents . however, these benchmarks fail to capture the challenges posed by documents in the real world .
Approach:	They propose a new benchmark that incorporates a diverse set of manually captured document images reflecting real-world conditions.
Outcome:	The proposed model is based on a set of manually captured document images reflecting real-world conditions and is compared with digital or scanned documents.

Training-Free Adaptive Speculative Decoding via Linguistic Priors (2026.findings-acl)

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Jingyi Wang, Jiaqi Huang, Zunnan Xu, Jun Zhou, Kehong Yuan, Xiang Qian

Syntactic Text Processing

Challenge:	Speculative decoding (SPD) is a promising technique to accelerate Large Language Models (LLMs). current approaches neglect the inherent heterogeneity of natural language and fail to distinguish between semantically-rich content and structurally-predictable syntax.
Approach:	They propose a training-free framework that leverages linguistic priors to enable adaptive drafting and verification.
Outcome:	The proposed framework significantly accelerates inference without additional training.

Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024.lrec-main)

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Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao

Reasoning with Language Model Prompting: A Survey (2023.acl-long)

Challenge:	Existing methods to detect causal relationships in unstructured texts ignore trivial knowledge which may prejudice performance.
Approach:	They propose a pipeline to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsens graphs.
Outcome:	The proposed pipeline integrates reliable task-specific knowledge from commonsense graphs.

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Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation (2024.findings-acl)

Challenge:	Reasoning is an essential ability for complex problem-solving and can provide back-end support for various real-world applications.
Approach:	They present cutting-edge research on reasoning with language model prompting and provide systematic resources to help beginners.
Outcome:	The proposed approaches have not been systematically reviewed and analyzed.

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Xu Huang, Zhirui Zhang, Xiang Geng, Yichao Du, Jiajun Chen, Shujian Huang

Multilinguality

Challenge:	Recent studies have shown that Large Language Models (LLMs) can be used as translation evaluators.
Approach:	They propose to use both coarse-grained and fine-grounded prompts to discern the utility of source versus reference data in machine translation evaluation tasks.
Outcome:	The proposed model can be used to evaluate translations in multiple languages.

Training Language Model to Critique for Better Refinement (2025.findings-acl)

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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang

Teaching Machine Comprehension with Compositional Explanations (2020.findings-emnlp)

Challenge:	Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks.
Approach:	They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses.
Outcome:	The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes.

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Qinyuan Ye, Xiao Huang, Elizabeth Boschee, Xiang Ren

Explainability & Interpretability in NLP

Challenge:	Recent advances in machine reading comprehension rely heavily on large-scale annotated corpora, which are timeconsuming and costly to collect.
Approach:	They propose to use semi-structured explanations to “teach” machines reading comprehension using a small number of semi-structural explanations that explicitly inform machines why answer spans are correct.
Outcome:	The proposed method achieves 70.14% F1 score with supervision from 26 explanations on the SQuAD dataset, comparable to plain supervised learning using 1,100 labeled instances yielding a 12x speed up.

Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)

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Yuxin Huang, Simeng Wu, Ran Song, Yan Xiang, Yantuan Xian, Shengxiang Gao, Zhengtao Yu

Multilinguality

Challenge:	Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation.
Approach:	They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space.
Outcome:	The proposed framework improves cross-lingual alignment and reduces redundancy.

Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)

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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang

Text Generation

Challenge:	evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT).
Approach:	They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations .
Outcome:	The proposed model outperforms strong baselines in both supervised and unsupervised settings.

Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)

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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam

Dialogue Systems & Conversational Agents

Challenge:	a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions.
Approach:	They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models .
Outcome:	The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year .

RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)

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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth

PathoHR: Hierarchical Reasoning for Vision-Language Models in Pathology (2025.findings-emnlp)

Challenge:	Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains.
Approach:	They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control .
Outcome:	The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control.

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Yating Huang, Ziyan Huang, Lintao Xiang, Qijun Yang, Hujun Yin

Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)

Challenge:	Current vision-language (VL) models fail to capture complex reasoning required for interpreting structured pathological reports.
Approach:	They propose a pathology-specific VL training scheme that generates enhanced and perturbed samples for multimodal contrastive learning.
Outcome:	The proposed approach achieves state-of-the-art performance on PathoHR-Bench and six additional pathology datasets, highlighting its effectiveness in fine-grained pathology representation.

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Rong Xiang, Yunfei Long, Mingyu Wan, Jinghang Gu, Qin Lu, Chu-Ren Huang

Sentiment Analysis

Challenge:	Existing deep neural network models lack mechanisms to highlight important sentiment terms.
Approach:	They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms.
Outcome:	The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets.

Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)

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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang

MarkQA: A large scale KBQA dataset with numerical reasoning (2023.emnlp-main)

Challenge:	Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals.
Approach:	They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals.
Outcome:	The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations.

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Xiang Huang, Sitao Cheng, Yuheng Bao, Shanshan Huang, Yuzhong Qu

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)

Challenge:	Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning.
Approach:	They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning.
Outcome:	The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning.

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Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Jun Huang, Ming Gao, Xiang Li

Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics (2022.findings-acl)

Challenge:	Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated.
Approach:	They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty.
Outcome:	The proposed framework improves performance and efficiency over multiple tasks over multiple datasets.

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Jiannan Xiang, Huayang Li, Yahui Liu, Lemao Liu, Guoping Huang, Defu Lian, Shuming Shi

Machine Translation

Challenge:	Current evaluation methods focus on one dataset, e.g., Newstest dataset in each year’s WMT Metrics Shared Task.
Approach:	They propose to use a single dataset to evaluate the performance of automatic translation metrics.
Outcome:	The results show that the rankings of metrics vary when the evaluation is conducted on different datasets.

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding (2022.emnlp-main)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Jianing Wang, Wenkang Huang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Xiang Li, Ming Gao

Planning and Editing What You Retrieve for Enhanced Tool Learning (2024.findings-naacl)

Challenge:	Existing knowledge-enhanced pre-trained language models (PLMs) introduce redundant factual knowledge from knowledge bases and require complex modules.
Approach:	They propose a knowledge prompting-based PLM framework that incorporates factual knowledge into PLMs.
Outcome:	The proposed framework can be flexibly combined with existing mainstream PLMs.

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Tenghao Huang, Dongwon Jung, Vaibhav Kumar, Mohammad Kachuee, Xiang Li, Puyang Xu, Muhao Chen

Information Retrieval

Challenge:	Existing methods for integrating external tools with Large Language Models fall short on effectively shortlisting relevant tools.
Approach:	They propose a plan-and-retrieve and edit-and ground paradigms for LLMs that decompose complex queries into actionable tasks.
Outcome:	The proposed paradigms significantly improve recall and NDCG in tool retrieval tasks, surpassing current state-of-the-art models.

Visualizing the Relationship Between Encoded Linguistic Information and Task Performance (2022.findings-acl)

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Jiannan Xiang, Huayang Li, Defu Lian, Guoping Huang, Taro Watanabe, Lemao Liu

Syntactic Text Processing

Challenge:	Recent studies show that encoding more syntactic information does not lead to better performance.
Approach:	They propose a method to optimize pareto-optimal models by formalizing it as a multi-objective optimization problem.
Outcome:	The proposed method is better than a baseline method on two NLP tasks.

Analyzing and Internalizing Complex Policy Documents for LLM Agents (2026.acl-long)

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Jiateng Liu, Zhenhailong Wang, Xiaojiang Huang, Yingjie Li, Xiang Li, Chenlei Guo, Xing Fan, Ruhi Sarikaya, Heng Ji

Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training (2022.findings-acl)

Challenge:	Large language model agents rely on in-context policy documents to act as effective user assistants.
Approach:	They propose an agentic benchmark generator with Controllable Complexity in agent policy across four levels to evaluate agents under increasing complexity.
Outcome:	The proposed method outperforms the baseline in data-sparse and high-complexity settings.

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Peixin Huang, Xiang Zhao, Minghao Hu, Yang Fang, Xinyi Li, Weidong Xiao

Named Entity Recognition

Challenge:	Existing studies treat named entity recognition as a sequential labeling problem.
Approach:	They propose a span selection framework for nested named entity recognition . they propose nesting entities with different input categories would be separately extracted .
Outcome:	The proposed framework outperforms competing models on four benchmark datasets.

GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)

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Yang Chen, Shuwan Yang, Yan Xiang, Ran Song, Yuxin Huang, Zhengtao Yu

Sentiment Analysis

Challenge:	Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities.
Approach:	They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one .
Outcome:	The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs.

Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning (2025.findings-acl)

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Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang

LogiGraph: Logical Reasoning with Contrastive Learning and Lightweight Graph Networks (2025.coling-main)

Challenge:	Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL).
Approach:	They propose a solution that takes multiple compositions as inputs and constrains disentangled primitive features to be general across compositions.
Outcome:	The proposed architecture significantly improves performance on three popular CZSL benchmarks and has been verified by solid ablation studies.

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Xiang Li, Chen Shi, Yong Xu, Jun Huang

TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data (2025.acl-long)

Challenge:	Existing methods emphasize contextual semantics while others pay more attention to explicit logical features. Existing models utilize graph convolutional networks (GCN) for node updates, still exhibiting some shortcomings.
Approach:	They propose a logical reasoning method with contrastive learning and lightweight graph networks (LogiGraph) they employ conjunction and punctuation marks as two types of edges to construct a dual graph.
Outcome:	The proposed method improves the GCN and employs conjunction and punctuation marks as two types of edges to construct a dual graph.

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Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng, Xiaxia Wang, Yuzhong Qu

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

Challenge:	Existing methods for semantic parsing rely on extensive manually annotated datasets and limited generalization capability to unseen examples.
Approach:	They propose a framework that generates high-relevance synthetic data without manual annotation . they generate queries for the queries and use them as demonstrations for in-context learning .
Outcome:	The proposed framework outperforms non-fine-tuned methods on KBQA datasets and shows superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings.

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu

T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates (2023.tacl-1)

Challenge:	Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities .
Approach:	They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner.
Outcome:	The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard.

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Peixin Huang, Xiang Zhao, Minghao Hu, Zhen Tan, Weidong Xiao

Reverse Preference Optimization for Complex Instruction Following (2025.findings-acl)

Challenge:	Named Entity Recognition (NER) has evolved from flat to overlapped and discontinuous . NER is a text recognition task that recognizes mentions that represent entities in text .
Approach:	They propose a two-stage span-based framework to solve a unified NER task using two stages . they extract entity spans, classify over all entity span pairs and combine them to train two stages.
Outcome:	The proposed framework beats all the current competitive baselines on eight benchmark datasets, obtaining the best performance of unified NER.

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Xiang Huang, Ting-En Lin, Feiteng Fang, Yuchuan Wu, Hangyu Li, Yuzhong Qu, Fei Huang, Yongbin Li

Exploring Better Text Image Translation with Multimodal Codebook (2023.acl-long)

Challenge:	Existing methods for identifying and evaluating preference pairs with multiple constraints are noisy.
Approach:	They propose a method that dynamically reverses constraints to ensure the chosen response is perfect.
Outcome:	The proposed method reduces noise in preference pairs by reversing constraints to ensure the chosen response is perfect.

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Zhibin Lan, Jiawei Yu, Xiang Li, Wen Zhang, Jian Luan, Bin Wang, Degen Huang, Jinsong Su

Beyond [CLS] through Ranking by Generation (2020.emnlp-main)

Challenge:	Current studies on text image translation face bottlenecks due to lack of a publicly available dataset and poor optical character recognition.
Approach:	They propose a text image translation model with a multimodal codebook and an OCR dataset for Chinese-English translation.
Outcome:	The proposed model can associate the image with relevant texts, providing useful supplementary information for translation.

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Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, Bing Xiang

Information Retrieval

Challenge:	Recent work on generative ranking models for Information Retrieval has focused on discriminative methods that learn a similarity function to compare questions and candidates answers.
Approach:	They propose to use a language model to train a ranking function that model the semantic similarity of documents and queries instead of discriminative ranking functions.
Outcome:	The proposed approaches are as effective as state-of-the-art discriminative models for the answer selection task and show unlikelihood losses are reduced for IR.

QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)

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Xiang Huang, Sitao Cheng, Shanshan Huang, Jiayu Shen, Yong Xu, Chaoyun Zhang, Yuzhong Qu

AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)

Challenge:	Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction .
Approach:	They propose a framework that solves a query step-by-step and performs stepwise self-correction.
Outcome:	The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points.

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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu

Joint Event Extraction with Hierarchical Policy Network (2020.coling-main)

Challenge:	Significant concerns emerge when addressing cultural sensitivity and local values.
Approach:	They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models.
Outcome:	The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks.

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Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, Weidong Xiao

Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)

Challenge:	Existing work on event extraction (EE) is pipelined or uses a joint structure but does not utilize information interactions among event triggers, event arguments, and argument roles.
Approach:	They propose to exploit role information of arguments in an event and devise a Hierarchical Policy Network to perform joint EE.
Outcome:	The proposed system outperforms existing methods and is more powerful for sentences with multiple events.

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Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng, Yanjie Wang, Wei Shi, An-Lan Wang, Jingqun Tang, Can Huang

Challenge:	Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences.
Approach:	They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences.
Outcome:	Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs.

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)

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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

Natural Language Interfaces

Challenge:	ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets.
Approach:	They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation.
Outcome:	The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models.

CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)

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Zhenhui Ye, Rongjie Huang, Yi Ren, Ziyue Jiang, Jinglin Liu, Jinzheng He, Xiang Yin, Zhou Zhao

CO-EVO: Co-evolving Semantic Anchoring and Style Diversification for Federated DG-ReID (2026.acl-long)

Challenge:	Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks.
Approach:	They propose a cross-modal contrastive pre-training framework that learns from prosody variance of the same text token under different contexts.
Outcome:	The proposed framework can learn from prosody variance of a text token under different contexts.

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null Fengchunzhang, Qiang Ma, Liuyu Xiang, Jinshan Lai, Tingxuan Huang, Jianwei Hu

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)

Challenge:	Existing frameworks for person re-identification fail to provide global supervision . stylistic gaps in the model can lead to shortcut learning .
Approach:	They propose a framework that aims to generalize a person's identity across multiple decentralized domains.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance . it can generalize to unseen target environments without compromising privacy .

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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao

Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)

Challenge:	Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech.
Approach:	They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness.
Outcome:	The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines .

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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation (2025.findings-naacl)

Challenge:	Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution.
Approach:	They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research .
Outcome:	The proposed model can be used to analyze the evolution of parametric knowledge in LLMs.

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Qilong Wu, Xiaoneng Xiang, Huang Hejia, Xuan Wang, Yeo Wei Jie, Ranjan Satapathy, Ricardo Shirota Filho, Bharadwaj Veeravalli

SimulBench: Evaluating Language Models with Creative Simulation Tasks (2025.findings-naacl)

Challenge:	Existing tools for financial reporting and ESG analysis are lacking . large language models are not proficient across general finance and ESE domains .
Approach:	They propose a dataset that includes seven financial NLP tasks and a benchmark to improve sustainability report generation.
Outcome:	SusGen-30k, a high-quality dataset, shows state-of-the-art performance . it surpasses all other models except GPT-4 in six adapted tasks and two off-the shelf tasks .

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Qi Jia, Xiang Yue, Tuney Zheng, Jie Huang, Bill Yuchen Lin

Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource (2020.aacl-main)

Challenge:	Existing benchmarks for large language models do not fully evaluate their potential for broad implementation.
Approach:	They propose to use a fixed LLM as a user agent to engage with an LLM to collect dialogues first under different tasks.
Outcome:	The proposed framework outperforms LLaMA-3-70b-Chat on 18.55% more cases.

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Rong Xiang, Mingyu Wan, Qi Su, Chu-Ren Huang, Qin Lu

Challenge:	Mandarin Alphabetical Words (MAWs) are a key component of Modern Chinese . they are characterized by unique code-mixing idiosyncrasies influenced by language exchanges .
Approach:	They propose to construct a large collection of Mandarin Alphabetic Words from Sina Weibo . they propose to use a web-based technique to identify and validate MAWs .
Outcome:	The proposed method identifies 16,207 Mandarin Alphabetic Words (MAWs) using a web-based technique . the results show that the proposed method is useful for linguistic research and inquiries .

GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering (2025.coling-main)

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Qianlong Li, Chen Huang, Shuai Li, Yuanxin Xiang, Deng Xiong, Wenqiang Lei

Structured Data in NLP

Challenge:	Existing methods for complex table question answering are often implicit, feeding the entire table into prompts.
Approach:	They propose a GraphOTTER that explicitly establishes the reasoning process to pinpoint the correct answers.
Outcome:	The proposed method is able to identify the correct answers on two benchmark datasets and two LLM backbones.

Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling (2020.acl-main)

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Ouyu Lan, Xiao Huang, Bill Yuchen Lin, He Jiang, Liyuan Liu, Xiang Ren

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Existing methods for Sequence Labeling require high-quality annotations, but imperfect annotations are relatively easy to obtain from crowdsourcing (noisy labels) Existing approaches to learn a model without knowing the underlying ground truth label sequences in the target domain are expensive and time-consuming.
Approach:	They propose a framework Consensus Network that can be trained on annotations from multiple sources.
Outcome:	The proposed framework improves on learning with crowd annotations and unsupervised cross-domain model adaptation in two practical settings.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang

Measure Children’s Mindreading Ability with Machine Reading (2023.findings-emnlp)

Challenge:	Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs.
Approach:	They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding.
Outcome:	The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities.

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Yuliang Yan, Xiaohua Wang, Xiang Zhou, Xiaoqing Zheng, Xuanjing Huang

Visual Data in NLP

Challenge:	Existing scoring models do not take the features of the stories and video clips into account when scoring, which will reduce the accuracy of the models.
Approach:	They propose to leverage the features extracted from stories and videos related to the questions being asked during the children’s mindreading evaluation.
Outcome:	The proposed framework agrees well with human experts on scores produced by the models.

Improve Transformer Models with Better Relative Position Embeddings (2020.findings-emnlp)

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Zhiheng Huang, Davis Liang, Peng Xu, Bing Xiang

ConvLab: Multi-Domain End-to-End Dialog System Platform (P19-3)

Challenge:	Existing methods for generating position embeddings are not fully utilized in NLP tasks.
Approach:	They propose to generalize the absolute position embedding to a generalized relative position embedded method . they also propose to use the relative embeddable method to improve the accuracy of large models .
Outcome:	The proposed method improves accuracy on the SQuAD1.1 dataset compared to previous methods . it can be easily adopted as a drop-in replacement for improving accuracy of large models .

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Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

Dialogue Systems & Conversational Agents

Challenge:	ConvLab is an open-source multi-domain end-to-end dialog system platform . it allows researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments.
Approach:	They propose to use an open-source multi-domain end-to-end dialog system platform to train and evaluate dialog bots in common environments.
Outcome:	The proposed system enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches in common environments.

Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025.emnlp-main)

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Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris, Ruoyu Xiang, Yan Wang, Lingfei Qian, Jimin Huang, Qianqian Xie, Sophia Ananiadou

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)

Challenge:	Greek is the dominant language of the world's merchant navy and is a key language for international trade.
Approach:	They propose to develop a Greek financial evaluation benchmark and a financial LLM fine-tuned on Greek-specific financial data to bridge this gap.
Outcome:	The proposed benchmarks surpass GPT-4 by 8.33%, GPT- 4o by 26.83%, and Deepseek-V3 by 67.74%.

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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang