Papers by Xinyu Huang

Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention (N18-1)

Challenge:	Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges.
Approach:	They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments .
Outcome:	The proposed framework and evaluator are competitive in counter-argument generation tasks.

Copied to clipboard

Huadong Chen, Shujian Huang, David Chiang, Xinyu Dai, Jiajun Chen

Machine Translation

Challenge:	Neural machine translation models learn to map from source language sentences to target language sentences via continuous-space intermediate representations.
Approach:	They propose an encoder with character attention which augments the (sub)word-level representation with character-level information and a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively.
Outcome:	The proposed model outperforms the standard word-based model, subword-based models, and strong character-based ones on translation tasks.

Task Oriented In-Domain Data Augmentation (2024.emnlp-main)

Copied to clipboard

Xiao Liang, Xinyu Hu, Simiao Zuo, Yeyun Gong, Qiang Lou, Yi Liu, Shao-Lun Huang, Jian Jiao

Numerical Reasoning

Challenge:	Existing methods for large language models suffer from two major issues: in-domain data are scarce compared with general domain-agnostic data.
Approach:	They propose a task-oriented in-domain data augmentation framework that uses in- domain data selection and task-orientated synthetic passage generation to adapt LLMs to two domains: advertisement and math.
Outcome:	The proposed framework improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain.

Towards Efficient NLP: A Standard Evaluation and A Strong Baseline (2022.naacl-main)

Copied to clipboard

Xiangyang Liu, Tianxiang Sun, Junliang He, Jiawen Wu, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu

Explicit Semantic Decomposition for Definition Generation (2020.acl-main)

Challenge:	Rather than pursuing the reachless SOTA accuracy, researchers are focusing on model efficiency and usability.
Approach:	They propose an evaluation and a public leaderboard for efficient NLP models that depicts the Pareto Frontier for various language understanding tasks.
Outcome:	The proposed model outperforms or performs on par with SOTA compressed and early exiting models.

Copied to clipboard

Jiahuan Li, Yu Bao, Shujian Huang, Xinyu Dai, Jiajun Chen

Challenge:	Existing definition generation methods rely on decoding to extract semantic components of words.
Approach:	They propose a method which explicitly decomposes meaning of words into semantic components and models them with discrete latent variables for definition generation.
Outcome:	The proposed method outperforms existing methods on WordNet and Oxford benchmarks.

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

Copied to clipboard

Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency (2025.findings-acl)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

Copied to clipboard

Junzhe Zhang, Huixuan Zhang, Xunjian Yin, Baizhou Huang, Xu Zhang, Xinyu Hu, Xiaojun Wan

Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)

Challenge:	Existing benchmarks for knowledge editing in multimodal large language models focus on limited scenarios due to the lack of rigorous definition of multimodal knowledge.
Approach:	They propose a decomposed definition of multimodal knowledge and a benchmark to evaluate it.
Outcome:	The proposed method reveals that it is difficult to define multimodal knowledge editing in LLMs.

Copied to clipboard

Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)

Challenge:	Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention.
Approach:	They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task.
Outcome:	The proposed framework outperforms state-of-the-art on two public datasets.

Copied to clipboard

Zewei Sun, Shujian Huang, Xinyu Dai, Jiajun Chen

Machine Translation

Challenge:	Recent studies show that the attention heads in Transformer are not equal.
Approach:	They propose a masking method to mask attention heads in Transformer . they empirically validate the inequality and propose 'head mask' method to avoid bottleneck .
Outcome:	The proposed masking method improves translation performance on multiple languages . it can be used to remove a small subset of heads without affecting performance .

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

Copied to clipboard

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.

PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning (2025.acl-long)

Copied to clipboard

Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu

STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)

Challenge:	Large language models demonstrate remarkable capabilities across various domains, including mathematics and logic reasoning.
Approach:	They propose a physics-based reasoning benchmark that includes physics theorems and constraints and a Physics Solution Auto Scoring Framework to evaluate physics based reasoning in large language models.
Outcome:	The proposed framework enables models to achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.99%).

Copied to clipboard

Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma

Reasoning

Challenge:	Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation.
Approach:	They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset.
Outcome:	The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations.

KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)

Copied to clipboard

Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang

Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)

Challenge:	Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question .
Approach:	They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance .
Outcome:	The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered .

Copied to clipboard

Libo Sun, Siyuan Wang, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, Zhongyu Wei

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021.acl-long)

Challenge:	Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges .
Approach:	They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation .
Outcome:	The proposed framework improves product review summarization with forward reasoning and backward refinement.

Copied to clipboard

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Information Extraction & Text Mining

Challenge:	Recent work shows document-level contexts can significantly improve Named Entity Recognition models.
Approach:	They propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine with the original sentence as the query.
Outcome:	The proposed approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.

Structure-Unified M-Tree Coding Solver for Math Word Problem (2022.emnlp-main)

Copied to clipboard

Bin Wang, Jiangzhou Ju, Yang Fan, Xinyu Dai, Shujian Huang, Jiajun Chen

Numerical Reasoning

Challenge:	Existing models that take into account the binary tree structure of mathematical expressions have achieved better performance, but the output space is non-deterministic.
Approach:	They propose a Structure-Unified M-Tree Coding Solver which applies a tree with any M branches to unify the output structures.
Outcome:	The proposed model outperforms several state-of-the-art models under similar experimental conditions and performs much better under low-resource conditions.

CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis (2025.findings-emnlp)

Copied to clipboard

Xinyu Zhang, Pei Zhang, Shuang Luo, Jialong Tang, Yu Wan, Baosong Yang, Fei Huang

Information Retrieval

Challenge:	Cultural competence is defined as the ability to understand and adapt to multicultural contexts.
Approach:	They propose a framework that uses a hierarchical multilingual taxonomy and a Retrieval-Augmented Generation to synthesize culturally relevant question-answer pairs.
Outcome:	The proposed framework contains a hierarchical multilingual taxonomy covering 12 primary and 130 secondary topics and a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs.

MultiSQL: A Schema-Integrated Context-Dependent Text2SQL Dataset with Diverse SQL Operations (2024.findings-acl)

Copied to clipboard

Chunhui Li, Yifan Wang, Zhen Wu, Zhen Yu, Fei Zhao, Shujian Huang, Xinyu Dai

Natural Language Interfaces

Challenge:	Text2SQL is a task that translates natural language into SQL statements.
Approach:	They propose a task that translates natural language into SQL statements.
Outcome:	The proposed task enables users to convert natural language into SQL statements.

PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment (2024.emnlp-main)

Copied to clipboard

Jiahuan Li, Shujian Huang, Aarron Ching, Xinyu Dai, Jiajun Chen

Multilinguality

Challenge:	Large language models exhibit reasonable multilingual abilities, despite predominantly English-centric pretraining.
Approach:	They propose a framework that establishes multilingual alignment prior to language model pretraining and preserves this alignment using a code-switching strategy during pretraining.
Outcome:	Experiments in a synthetic English to English-Clone setting show that PreAlign outperforms standard multilingual joint training in language modeling, zero-shot cross-lingual transfer, and cross-linguistic knowledge application.

A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)

Copied to clipboard

Wei Zou, Shujian Huang, Jun Xie, Xinyu Dai, Jiajun Chen

Robustness in NLP

Challenge:	Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems.
Approach:	They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric.
Outcome:	The proposed paradigm produces stable attacks with meaning-preserving adversarial examples.

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

Copied to clipboard

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

Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)

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.

Copied to clipboard

Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang

Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)

Challenge:	Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently .
Approach:	They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model.
Outcome:	The proposed framework renders long texts into compact visual pages and processes them with a vision-language model.

Copied to clipboard

Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen

Cross-Lingual Transfer

Challenge:	Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints.
Approach:	They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media.
Outcome:	The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method.

MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning (2026.findings-acl)

Copied to clipboard

Juexiang Ye, Xue Li, Yang Xinyu, Chengkai Huang, Lanshun Nie, Lina Yao, Dechen Zhan

Information Retrieval

Challenge:	Existing methods rely on unstructured retrieval or coarse abstractions, which lead to temporal conflicts, brittle reasoning, and limited traceability.
Approach:	They propose a unified memory framework that consolidates long-term agent experiences into three interconnected components that combine structured knowledge and evidence to construct compact yet information-dense contexts for reasoning.
Outcome:	The proposed framework significantly improves multi-hop and temporal reasoning accuracy while reducing input context length by over 95% compared to long-context baselines.

Diagram-Driven Course Questions Generation (2025.emnlp-main)

Copied to clipboard

Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang, Wenjun Wu, Bo Li, Shaowei Wang, Basura Fernando, Jun Liu

Visual Data in NLP

Challenge:	Visual Question Generation (VQG) research focuses on natural images while neglecting diagrams, a critical component of educational materials.
Approach:	They propose a diagram-driven course questions generation task to generate diagram-relevant questions for specific courses.
Outcome:	The proposed framework outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets.

MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning (2026.acl-long)

Copied to clipboard

Weikang Shi, Aldrich Yu, Rongyao Fang, Houxing Ren, Ke Wang, Aojun Zhou, Changyao Tian, Xinyu Fu, Yuxuan Hu, Zimu Lu, Linjiang Huang, Si Liu, Rui Liu, Hongsheng Li

Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models (2025.findings-emnlp)

Challenge:	Existing approaches to visual chain-of-thought are limited by external tools or fail to generate high-fidelity diagrams.
Approach:	They propose a framework to enable large multimodal models with VCoT capabilities . they pre-train a model on a 15.2M-pair corpus and teach it how to leverage visual aids .
Outcome:	The proposed framework unlocks complex, human-like visual reasoning in large language models . it pre-trains the model on a 15.2M-pair corpus and fine-tunes it on MathCanvas-Instruct .

Copied to clipboard

Runsong Zhao, Xin Liu, Xinyu Liu, Pengcheng Huang, Chunyang Xiao, Tong Xiao, JingBo Zhu

Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor (2021.acl-long)

Challenge:	Existing methods to compress context information ignore holistic contextual dependencies.
Approach:	They propose a method that adjusts position encodings to minimize the distance between context tokens and special tokens.
Outcome:	Enhanced Position Layout (EPL) improves compression of context information in large language models.

Copied to clipboard

Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Green & Sustainable NLP

Challenge:	Knowledge distillation is a technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student).
Approach:	They propose a factorized form of the knowledge distillation objective for structured prediction which is tractable for many typical choices of the teacher and student models.
Outcome:	The proposed model is able to transfer knowledge between teacher and student models without loss of accuracy under four different scenarios.

EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)

Copied to clipboard

Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang

Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining (2023.emnlp-main)

Challenge:	Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios.
Approach:	They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts.
Outcome:	The proposed model performs comparable to state-of-the-art large models on the test set.

Copied to clipboard

Jingcong Liang, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei

Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)

Challenge:	Recent studies have discussed its capability to assist language models for various applications.
Approach:	They propose a structure to organize arguments using the Hierarchical Argumentation Graph (Hi-ArG) and propose two approaches to exploit Hi-AarG, including a text-graph multi-modal model GreaseArR and a framework augmented with graph information.
Outcome:	The proposed structure supersedes existing language models on two argumentation tasks while incorporating graph information during further training improves vanilla language models.

Copied to clipboard

Sen Yang, Shujian Huang, Wei Zou, Jianbing Zhang, Xinyu Dai, Jiajun Chen

Explainability & Interpretability in NLP

Challenge:	Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features.
Approach:	They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations.
Outcome:	The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation.

Automated Concatenation of Embeddings for Structured Prediction (2021.acl-long)

Copied to clipboard

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Representation Learning

Challenge:	Recent work shows that better word representations can be obtained by concatenating different types of embeddings.
Approach:	They propose to automate the process of finding better concatenated embeddings for structured prediction tasks by concatending different types of embeddables.
Outcome:	The proposed approach outperforms baselines and achieves state-of-the-art with fine-tuned embeddings on 6 tasks and 21 datasets.

Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-Context Models (2024.emnlp-main)

Copied to clipboard

Xinyu Liu, Runsong Zhao, Pengcheng Huang, Chunyang Xiao, Bei Li, Jingang Wang, Tong Xiao, JingBo Zhu

TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)

Challenge:	Existing methods, tasks and benchmarks to measure model’s effective memory length are limited.
Approach:	They propose a method called forgetting curve to measure the memorization capability of long-context models.
Outcome:	The proposed method is robust to the tested corpus and experimental settings, and can be applied to any model size.

Copied to clipboard

Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao

Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference (2025.emnlp-main)

Challenge:	Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors.
Approach:	They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages.
Outcome:	The proposed model can translate audio-visual speech into audio-visual speech in other languages.

Copied to clipboard

Zhuo Chen, Xinyu Wang, Yong Jiang, Zhen Zhang, Xinyu Geng, Pengjun Xie, Fei Huang, Kewei Tu

Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)

Challenge:	Existing methods to detect the knowledge boundary of Vision Large Language Models (VLLMs) are expensive and require indiscriminate retrieval to address questions that require real-time information or are knowledge-intensive.
Approach:	They propose a method that fine-tunes a VLLM on an automatically constructed dataset for boundary identification.
Outcome:	The proposed method reduces indiscriminate retrieval while maintaining or improving the performance of a VLLM on an automatically constructed dataset.

Copied to clipboard

Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, Jiajun Chen

Addressing Linguistic Bias through a Contrastive Analysis of Academic Writing in the NLP Domain (2023.emnlp-main)

Challenge:	Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables .
Approach:	They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs .
Outcome:	The proposed model achieves comparable or better performance in machine translation tasks than strong baselines.

Copied to clipboard

Robert Ridley, Zhen Wu, Jianbing Zhang, Shujian Huang, Xinyu Dai

Responsible & Trustworthy NLP

Challenge:	a reviewer’s opinion of the nativeness of expression in an academic paper affects the likelihood of it being accepted for publication.
Approach:	They conduct a statistical analysis of paper abstracts from the natural language processing domain to identify how authors from different linguistic backgrounds differ in the lexical, morphological, syntactic and cohesive aspects of their writing.
Outcome:	The results suggest that there is potential for linguistic bias in the domain of natural language processing.

Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (P19-1)

Copied to clipboard

Xinyu Wang, Jingxian Huang, Kewei Tu

Syntactic Text Processing

Challenge:	Existing approaches to semantic dependency parsing are graph-based and transition-based.
Approach:	They propose a second-order semantic dependency parser which takes relationships between two or more edges into account.
Outcome:	The proposed algorithm outperforms existing approaches to parsing on graph-based approaches.

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

Copied to clipboard

Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

Information Retrieval

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)

Copied to clipboard

Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Kewei Tu

Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)

Challenge:	Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations.
Approach:	They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts .
Outcome:	The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information.

Copied to clipboard

Zheng Ma, Shi Zong, Mianzhi Pan, Jianbing Zhang, Shujian Huang, Xinyu Dai, Jiajun Chen

Structure-Level Knowledge Distillation For Multilingual Sequence Labeling (2020.acl-main)

Challenge:	In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.
Approach:	They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models.
Outcome:	The proposed method analyzes captions generated by five popular VLP models to reveal how well they align with visual words and how well these align with images.

Copied to clipboard

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu

Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (2021.emnlp-main)

Challenge:	Existing multilingual models still underperform individual monolingual models due to model capacity limitations.
Approach:	They propose to distill the structural knowledge of several monolingual models (teachers) to the unified multilingual model (student).
Outcome:	The proposed model outperforms strong baseline models and teacher models on 4 multilingual tasks with 25 datasets and has stronger zero-shot generalizability.

Copied to clipboard

Ran Wang, Xi’ao Su, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

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

Challenge:	Large-scale multi-label text classification tasks often face long-tailed label distributions, where many labels have few or even no training instances.
Approach:	They propose a meta-learning approach that incorporates the objective of adapting to new low-resource tasks into the meta-Learning phase.
Outcome:	The proposed approach achieves state-of-the-art against strong baselines and can still enhance powerful BERTlike models.

Copied to clipboard

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

GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)

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.

Copied to clipboard

Zixian Huang, Yulin Shen, Xiao Li, Yu’ang Wei, Gong Cheng, Lin Zhou, Xinyu Dai, Yuzhong Qu

Question Answering

Challenge:	SQA is an emerging application of NLP in the medical, geography, and legal domains.
Approach:	They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level.
Outcome:	The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level.

MGPO: Thinking with Images via Multi-Turn Grounding-Based Reinforcement Learning (2026.findings-acl)

Copied to clipboard

Xinyu Huang, Yuhao Dong, Weiwei Tian, Bo Li, Rui Feng, Ziwei Liu

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)

Challenge:	State-of-the-art large multimodal models face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task.
Approach:	They propose a multi-turn grounding-based policy optimization framework that enables LMMs to iteratively focus on key visual regions by automatically cropping sub-images based on model-predicted grounding coordinates within a multiple-turn conversation framework.
Outcome:	The proposed framework improves on Qwen2.5-VL-7B with 21K samples and surpasses OpenAI’s o1 and GPT-4o models on the out-of-distribution (OOD) V* Bench.

Copied to clipboard

Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang

Natural Language Interfaces

Challenge:	Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well.
Approach:	They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet.
Outcome:	The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future.

More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)

Copied to clipboard

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)

Challenge:	Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective .
Approach:	They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages .
Outcome:	The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders.

Copied to clipboard

Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li

When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation (2021.acl-short)

Challenge:	Recent advances in text generation have limited applications due to multimodality problem.
Approach:	They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem.
Outcome:	The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

Copied to clipboard

Jiahuan Li, Yutong Shen, Shujian Huang, Xinyu Dai, Jiajun Chen

Machine Translation

Challenge:	Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules.
Approach:	They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena .
Outcome:	The character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains.

Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (P19-1)

Copied to clipboard

Zi Chai, Xinyu Xing, Xiaojun Wan, Bo Huang

Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM (2024.findings-acl)

Challenge:	Existing work on teaching machines to ask questions focused on generating fixed answers.
Approach:	They propose a model to generate open-answered questions from real-world news for open discussion . they analyze how language use affects the number of answers .
Outcome:	The proposed model generates questions with higher quality than most text generation methods.

Copied to clipboard

Jingcong Liang, Rong Ye, Meng Han, Ruofei Lai, Xinyu Zhang, Xuanjing Huang, Zhongyu Wei

Challenge:	Recent studies have focused on short dialogues, but mainly on short debates.
Approach:	They propose to use Large Language Models to construct an automated debate judge to evaluate multi-turn debates.
Outcome:	The proposed system improves on the PanelBench benchmark, which compares its performance to actual debate outcomes.

Solving Math Word Problems via Cooperative Reasoning induced Language Models (2023.acl-long)

Copied to clipboard

Xinyu Zhu, Junjie Wang, Lin Zhang, Yuxiang Zhang, Yongfeng Huang, Ruyi Gan, Jiaxing Zhang, Yujiu Yang

Reasoning

Challenge:	Large-scale pre-trained language models (PLMs) can be used to solve math word problems, but they lack fast adaptivity as humans.
Approach:	They propose a cooperative reasoning-induced PLM for solving the math word problem . they use system 1 as the generator and system 2 as the verifier to generate reasoning paths .
Outcome:	The proposed model improves on several mathematical reasoning datasets and achieves 9.6% improvement over baselines.

RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)

Copied to clipboard

Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Dialogue State Tracking with Explicit Slot Connection Modeling (2020.acl-main)

Challenge:	Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries.
Approach:	They propose a framework for training query rewriting models that leverages a reranker framework.
Outcome:	The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models.

Copied to clipboard

Yawen Ouyang, Moxin Chen, Xinyu Dai, Yinggong Zhao, Shujian Huang, Jiajun Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to track dialogue state are lacking in multi-domain scenarios.
Approach:	They propose a model that explicitly considers slot correlations across domains . they propose ellipsis and reference to express values that have been mentioned by slots from other domains.
Outcome:	The proposed model outperforms existing models on multi-domain datasets and achieves state-of-the-art performance.

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network (2020.emnlp-main)

Copied to clipboard

Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Challenge:	Existing approaches to sequence labeling require sequential computation that makes parallelization impossible.
Approach:	They propose to employ a parallelizable approximate variational inference algorithm for the CRF model.
Outcome:	The proposed approach improves decoding speed and accuracy with long sentences and is parallelizable for faster training and prediction.

Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)

Copied to clipboard

Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, Min Zhang

Challenge:	Existing methods for multi-turn self-reflection are limited by the Echo Trap problem . the model is limited by its inherent capabilities and repeats earlier reflections to preserve reward signals .
Approach:	They propose a tree-structured extension of GRPO for multi-turn self-reflection which enables more accurate advantage estimation.
Outcome:	The proposed method mitigates behavior collapse and improves performance across benchmarks.

Energy-based Unknown Intent Detection with Data Manipulation (2021.findings-acl)

Copied to clipboard

Yawen Ouyang, Jiasheng Ye, Yu Chen, Xinyu Dai, Shujian Huang, Jiajun Chen

Sentiment Analysis

Challenge:	Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
Approach:	They propose a framework to generate high-quality OOD utterances with importance weighTs (GOT) their framework is fine-tuned to detect out-of-distribution utterrances .
Outcome:	The proposed framework can achieve state-of-the-art results on two benchmark datasets.

OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference (2025.acl-long)

Copied to clipboard

Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang, Maosongcao Maosongcao, Jiaqi Wang, Weiyun Wang, Xinyu Fang, Wenhai Wang, Guangtao Zhai, Hua Yang, Haodong Duan, Kai Chen

Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (2024.findings-acl)

Challenge:	Existing open-source multi-modal large language models (MLLMs) focus on enhancing foundational capabilities, leaving a significant gap in human preference alignment.
Approach:	They propose a dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences.
Outcome:	The proposed dataset of 200K high-quality training samples improves human preference alignment while maintaining or enhancing performance on standard VQA benchmarks.

Copied to clipboard

Zhuo Chen, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, Kewei Tu