Papers by Xiaojie Wang

USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis (2023.acl-long)

Challenge:	Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns.
Approach:	They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD.
Outcome:	The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions .

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Zepeng Zhai, Hao Chen, Ruifan Li, Xiaojie Wang

Structured Data in NLP

Challenge:	Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency parsing . previous studies have cast it as a bottleneck because of overlap and discontinuity issues .
Approach:	They propose a bi-lexical dependency parsing graph and a table-filling scheme that addresses overlap and discontinuity issues.
Outcome:	The proposed framework outperforms state-of-the-art methods on benchmark datasets.

Grouped-Attention for Content-Selection and Content-Plan Generation (2021.findings-emnlp)

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Bayu Distiawan Trisedya, Xiaojie Wang, Jianzhong Qi, Rui Zhang, Qingjun Cui

Text Generation

Challenge:	Recent neural data-to-text generation models explicitly learn content-plan given a set of attributes as input.
Approach:	They propose a neural content-planner that captures local and global contexts . they use a token-level attention constrained within each input attribute .
Outcome:	The proposed model outperforms competitors by 4.92%, 4.70%, and 16.56% on real-world datasets.

MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction (2024.lrec-main)

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Baohang Zhou, Ying Zhang, Kehui Song, Hongru Wang, Yu Zhao, Xuhui Sui, Xiaojie Yuan

Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis (2021.acl-long)

Challenge:	Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data.
Approach:	They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors.
Outcome:	The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it.

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Ruifan Li, Hao Chen, Fangxiang Feng, Zhanyu Ma, Xiaojie Wang, Eduard Hovy

Sentiment Analysis

Challenge:	Existing methods to model relationships between aspects and opinion words are inefficient due to informal expressions and complexity of online reviews.
Approach:	They propose a dual graph convolutional networks model that considers complementarity of syntax structures and semantic correlations simultaneously.
Outcome:	The proposed model outperforms state-of-the-art methods on three public datasets and validates it.

Enhancing Visual Dialog Questioner with Entity-based Strategy Learning and Augmented Guesser (2021.findings-emnlp)

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Duo Zheng, Zipeng Xu, Fandong Meng, Xiaojie Wang, Jiaan Wang, Jie Zhou

Natural Language Interfaces

Challenge:	Existing methods to build a visual dialog (VD) Questioner do not provide explicit guidance for questioner to generate visually related and informative questions.
Approach:	They propose a Related entity enhanced Questioner that learns entity-based questioning strategy from human dialogs.
Outcome:	The proposed approach achieves state-of-the-art performance on image-guessing task and question diversity.

Multimodal Aspect-Based Sentiment Analysis under Conditional Relation (2025.coling-main)

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Xinjing Liu, Ruifan Li, Shuqin Ye, Guangwei Zhang, Xiaojie Wang

Multi-stage Pre-training over Simplified Multimodal Pre-training Models (2021.acl-long)

Challenge:	Existing methods to analyze social media sentiments rely on image-based aspects.
Approach:	They propose a multi-task framework to extract aspect terms from text-image pairs and identify their sentiments.
Outcome:	The proposed framework outperforms existing methods on a text-image dataset.

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Tongtong Liu, Fangxiang Feng, Xiaojie Wang

Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)

Challenge:	Existing multimodal pre-training models require large amounts of training data and have huge model sizes, making them impossible to apply in low-resource situations.
Approach:	They propose a multi-stage pre-training method which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train a model in stages.
Outcome:	The proposed method outperforms the original model in Image-Text Retrieval task and outperformed the original LXMERT model in downstream tasks.

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Hengtong Lu, Zhuoxin Han, Caixia Yuan, Xiaojie Wang, Shuyu Lei, Huixing Jiang, Wei Wu

Semantic Parsing

Challenge:	Existing work on slot filling uses labeled data from source domains to train a model for target domains.
Approach:	They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot.
Outcome:	The proposed method outperforms state-of-the-art models on multiple datasets and models.

Connecting Embeddings for Knowledge Graph Entity Typing (2020.acl-main)

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Yu Zhao, Anxiang Zhang, Ruobing Xie, Kang Liu, Xiaojie Wang

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents (2025.emnlp-main)

Challenge:	Existing knowledge graphs suffer from incompleteness and miss important facts, jeopardizing their usefulness in downstream tasks such as question answering.
Approach:	They propose a method which is trained by utilizing local typing knowledge from existing entity type assertions and global triple knowledge in KGs.
Outcome:	The proposed model favors inferences that agree with both entity type instances and triple knowledge in KGs.

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Haochen Sun, Shuwen Zhang, Lujie Niu, Lei Ren, Hao Xu, Hao Fu, Fangkun Zhao, Caixia Yuan, Xiaojie Wang

Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (2023.findings-emnlp)

Challenge:	Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks.
Approach:	They propose a new LLM-based Multi-Agent System benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments.
Outcome:	The proposed benchmark provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication.

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Yangyang Luo, Shiyu Tian, Caixia Yuan, Xiaojie Wang

Natural Language Interfaces

Challenge:	Recent research has explored how to improve the abilities of decision-making and question generation.
Approach:	They propose a pipeline framework that aligns the document and user-provided information in an explicit way, makes decisions using a lightweight many-to-many entailment reasoning module and generates follow-up questions based on the document.
Outcome:	The proposed framework achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC.

Co-VQA : Answering by Interactive Sub Question Sequence (2022.findings-acl)

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Ruonan Wang, Yuxi Qian, Fangxiang Feng, Xiaojie Wang, Huixing Jiang

Visual Data in NLP

Challenge:	Existing approaches to Visual Question Answering (VQA) answer questions directly, but people usually decompose a complex question into a sequence of simple sub questions.
Approach:	They propose a conversation-based VQA framework that decomposes questions into sub questions and answers them one-by-one.
Outcome:	The proposed framework achieves state-of-the-art on VQA 2.0 and VQA-CP v2 datasets.

CoTD-PO: Chain-of-Thought Distillation with Preference Optimization (2025.findings-emnlp)

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Lujie Niu, Haochen Sun, Fangkun Zhao, Sheng Chen, Zimeng Bai, Jiawei Zhang, Caixia Yuan, Xiaojie Wang

Enhancing Multilingual Reasoning via Steerable Model Merging (2026.findings-acl)

Challenge:	Existing methods for chain-of-thought distillation suffer from a distribution mismatch between teacher-generated training trajectories and the student model's own generative distribution.
Approach:	They propose a framework that shifts the training paradigm from passive imitation to active trajectory exploration by allowing students to sample their own answer paths.
Outcome:	The proposed method outperforms standard CoT distillation baselines while mitigating mode collapse and preserving semantic diversity.

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Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu, Zhijun Chen, Qianren Mao, Hongcheng Guo, Jiaheng Liu, Likang Xiao, Ming LI, Xiaojie Wang

Reasoning

Challenge:	Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Approach:	They propose a model merging framework that modulates the contribution of each source model.
Outcome:	Experiments show that the proposed model merging framework outperforms strong baselines on multilingual reasoning benchmarks across 21 different languages.

KG-Adapter: Enabling Knowledge Graph Integration in Large Language Models through Parameter-Efficient Fine-Tuning (2024.findings-acl)

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Shiyu Tian, Yangyang Luo, Tianze Xu, Caixia Yuan, Huixing Jiang, Chen Wei, Xiaojie Wang

Towards Unifying Reference Expression Generation and Comprehension (2022.emnlp-main)

Challenge:	Large language models (LLMs) are criticized for lack of expertise and knowledge conflict . KG-Adapter is a parameter-level KG integration method for decoder-only LLMs .
Approach:	They propose a parameter-level KG integration method based on parameter-efficient fine-tuning . they use KG-Adapter to integrate knowledge graphs with LLMs and perform joint reasoning .
Outcome:	The proposed method outperforms the current state-of-the-art method on four datasets for two different tasks.

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Duo Zheng, Tao Kong, Ya Jing, Jiaan Wang, Xiaojie Wang

Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (2025.acl-long)

Challenge:	Existing models for REG and REC have distinct inputs and connections between them . a new model for REg and reprehension is needed to solve these problems .
Approach:	They propose a unified model for REG and REC that fuses image, region and text . they propose Vision-conditioned Masked Language Modeling and Text-Conditioned Region Prediction .
Outcome:	The proposed model outperforms existing models on REG and REC tasks.

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Yuheng Lu, Bingshuo Qian, Caixia Yuan, Huixing Jiang, Xiaojie Wang

Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)

Challenge:	Existing approaches to mitigate catastrophic forgetting can be broadly categorized into data-based, architecture-based and learning-based methods.
Approach:	They propose a subspace regularization method on LoRA structure that imposes constraints on direction of updating matrix’s null space.
Outcome:	The proposed method reduces scale of output change while introducing minimal constraint on model capacity.

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Hao Chen, Zepeng Zhai, Fangxiang Feng, Ruifan Li, Xiaojie Wang

Sentiment Analysis

Challenge:	Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations.
Approach:	They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task.
Outcome:	The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset.

Phased Instruction Fine-Tuning for Large Language Models (2024.findings-acl)

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Wei Pang, Chuan Zhou, Xiao-Hua Zhou, Xiaojie Wang

Learn to Adapt for Generalized Zero-Shot Text Classification (2022.acl-long)

Challenge:	Existing methods to enhance pre-trained language models' ability to follow instructions are limited due to the simultaneous handling of varying instruction complexities.
Approach:	They propose a phased instruction fine-tuning method that posits that the transition of a pre-trained language model from simple next-word prediction to sophisticated instruction following is a gradual learning process.
Outcome:	The proposed method surpasses the one-off instruction fine-tuning method in win rate and validates the hypothesis of progressive alignment.

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Yiwen Zhang, Caixia Yuan, Xiaojie Wang, Ziwei Bai, Yongbin Liu

Information Retrieval

Challenge:	Existing methods for generalized zero-shot text classification generalize poorly since the learned parameters are only optimal for seen classes rather than for both classes.
Approach:	They propose a network that trains an adaptive classifier by using both seen and virtual unseen classes to simulate a generalized zero-shot learning scenario.
Outcome:	The proposed model outperforms several previous approaches on five text classification datasets.

Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation (2026.acl-long)

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Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang

Challenge:	Existing "LLM-as-a-judge" evaluation frameworks are limited by persona descriptions and are not generalizable to other tasks.
Approach:	They propose a framework that can automatically construct multiple evaluator personas with distinct dimensions from relevant text documents and instantiate LLM agents with the persona.
Outcome:	The proposed framework can believably simulate human evaluators . it extracts stakeholders' diverse perspectives from the provided research papers and constructs personas for the agents .

A Simple Model for Distantly Supervised Relation Extraction (2022.coling-1)

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Ziqin Rao, Fangxiang Feng, Ruifan Li, Xiaojie Wang

DialogueTRM: Exploring Multi-Modal Emotional Dynamics in a Conversation (2021.findings-emnlp)

Challenge:	Recent methods focus on exploiting bag representations with complex de-noising scheme to achieve remarkable performance.
Approach:	They propose a BERT-based Graph convolutional network model that exploits bag representations . their model extracts key information from each instance and constructs a bag graph .
Outcome:	The proposed model improves on two benchmark datasets, i.e., NYT10 and GDS.

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Yuzhao Mao, Guang Liu, Xiaojie Wang, Weiguo Gao, Xuan Li

AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)

Challenge:	Existing studies focus on the self and inter-personal dependencies in multi-modal conversations, but they ignore the temporal and spatial dependencies.
Approach:	They propose a Dialogue Transformer for simultaneously modeling the intra-modal and inter-modal emotion dynamics.
Outcome:	The proposed models outperform the state-of-the-art on three benchmark datasets.

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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu

Challenge:	Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios.
Approach:	They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios.
Outcome:	Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift.

Multimodal Recommendation Dialog with Subjective Preference: A New Challenge and Benchmark (2023.findings-acl)

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Yuxing Long, Binyuan Hui, Caixia Yuan, Fei Huang, Yongbin Li, Xiaojie Wang

A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)

Challenge:	Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario.
Approach:	They propose a multimodal task-oriented dialog dataset with subjective preferences and recommendation acts that is well-annotated with sales experts.
Outcome:	The proposed model is powered by a state-of-the-art multimodal model for these tasks.

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Baohang Zhou, Ying Zhang, Kehui Song, Wenya Guo, Guoqing Zhao, Hongbin Wang, Xiaojie Yuan

EvoMemKG: An Evolvable Memory Agent for Multi-hop Knowledge Graph Reasoning (2026.findings-acl)

Challenge:	Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image.
Approach:	They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs.
Outcome:	The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods.

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Shiyu Tian, Shuyue Xing, Zhuoxin Han, Caixia Yuan, Xiaojie Wang

Knowledge Representation

Challenge:	Existing methods for integrating knowledge graphs with large language models lack continuous learning capabilities.
Approach:	They propose an agent framework with a dynamic, evolvable memory mechanism specifically designed for KG reasoning.
Outcome:	EvoMemKG achieves state-of-the-art performance without training or tools . it achieves improvements of up to 20% over baseline on multi-hop queries .

Task-Oriented Clustering for Dialogues (2021.findings-emnlp)

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Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, Xiaojie Wang

Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization (2021.findings-emnlp)

Challenge:	Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them.
Approach:	They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations .
Outcome:	The proposed model outperforms baselines on three public datasets on all metrics.

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Junpeng Liu, Yanyan Zou, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Caixia Yuan, Xiaojie Wang