Papers by Nan Tang

Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder (2020.acl-main)

Challenge:	Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data.
Approach:	They conduct a thorough examination of pretrained model based unsupervised sentence embeddings.
Outcome:	The proposed approach improves on whitening-based vector normalization with less than 10 lines of code.

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Daya Guo, Duyu Tang, Nan Duan, Jian Yin, Daxin Jiang, Ming Zhou

CoSQA: 20,000+ Web Queries for Code Search and Question Answering (2021.acl-long)

Challenge:	Existing approaches for inferential text generation ignore context that is not explicitly provided . Existing models ignore background knowledge that provides crucial evidence to generate inferences .
Approach:	They propose an approach that automatically finds evidence for an event from a large text corpus and leverages it to guide the generation of inferential texts.
Outcome:	The proposed model generates inferential texts from a large text corpus and uses evidence to guide it.

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Junjie Huang, Duyu Tang, Linjun Shou, Ming Gong, Ke Xu, Daxin Jiang, Ming Zhou, Nan Duan

Information Retrieval

Challenge:	Using deep neural networks to find codes is difficult . we present a dataset that includes 20,604 labels for natural language queries and codes .
Approach:	They introduce a contrastive learning method to enhance text-code matching . they find that CoSQA improves the accuracy of code question answering by 5.1% .
Outcome:	The proposed method improves the accuracy of code question answering by 5.1% and improves by 10.5% on a CodeBERT model.

ContextBLIP: Doubly Contextual Alignment for Contrastive Image Retrieval from Linguistically Complex Descriptions (2024.findings-acl)

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Honglin Lin, Siyu Li, Guoshun Nan, Chaoyue Tang, Xueting Wang, Jingxin Xu, Rong Yankai, Zhouzhili Zhouzhili, Yutong Gao, Qimei Cui, Xiaofeng Tao

Visual Data in NLP

Challenge:	Existing approaches to image retrieval from contextual descriptions (IRCD) lag behind human performance in IRCD.
Approach:	They propose a method that relies on a doubly contextual alignment scheme for challenging IRCD.
Outcome:	The proposed method can yield comparable results with GPT-4V, despite fewer parameters.

Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)

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Daya Guo, Yibo Sun, Duyu Tang, Nan Duan, Jian Yin, Hong Chi, James Cao, Peng Chen, Ming Zhou

Asking Clarification Questions in Knowledge-Based Question Answering (D19-1)

Challenge:	Using question generation, we learn a semantic parser with 30% of the supervised training data.
Approach:	They propose to use question generation to learn a semantic parser with less supervised training data.
Outcome:	The proposed method improves the state-of-the-art model with less training data.

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Jingjing Xu, Yuechen Wang, Duyu Tang, Nan Duan, Pengcheng Yang, Qi Zeng, Ming Zhou, Xu Sun

Natural Language Interfaces

Challenge:	Existing clarification datasets with limited annotated examples do not address ambiguous phenomena.
Approach:	They propose a dataset that allows users to ask clarification questions using open-domain examples.
Outcome:	The proposed model achieves better performance than strong baselines and provides new challenges.

OpenT2T: An Open-Source Toolkit for Table-to-Text Generation (2024.emnlp-demo)

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Haowei Zhang, Shengyun Si, Yilun Zhao, Lujing Xie, Zhijian Xu, Lyuhao Chen, Linyong Nan, Pengcheng Wang, Xiangru Tang, Arman Cohan

DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)

Challenge:	Existing methods for table-to-text generation are limited and benchmarked on a limited number of datasets.
Approach:	They propose to use open-source tools to reproduce existing large language models for performance comparison and expedite the development of new models.
Outcome:	The proposed toolkit compares existing large language models on 9 table-to-text generation datasets and maintains a leaderboard to provide insights for future work.

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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani

Structured Data in NLP

Challenge:	Data-to-text annotations can be costly when dealing with tables with nontrivial structures.
Approach:	They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title.
Outcome:	The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables.

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text (2022.findings-acl)

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Siyuan Wang, Wanjun Zhong, Duyu Tang, Zhongyu Wei, Zhihao Fan, Daxin Jiang, Ming Zhou, Nan Duan

Responsible & Trustworthy NLP

Challenge:	Existing methods for logical reasoning of text focus on contextual semantics while struggling to explicitly model the logical inference process.
Approach:	They propose a logic-driven context extension framework and a data-driven augmentation algorithm that uses contrastive learning to better capture logical information.
Outcome:	The proposed framework outperforms existing methods on two benchmark datasets, ReClor and LogiQA.

MBA-RAG: a Bandit Approach for Adaptive Retrieval-Augmented Generation through Question Complexity (2025.coling-main)

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Xiaqiang Tang, Qiang Gao, Jian Li, Nan Du, Qi Li, Sihong Xie

Green & Sustainable NLP

Challenge:	Existing RAG frameworks either indiscriminately perform retrieval or rely on rigid single-label classifiers to select retrieval methods.
Approach:	They propose a framework that dynamically selects the most suitable retrieval strategy based on query complexity.
Outcome:	The proposed framework achieves state-of-the-art results on multiple single-hop and multi-hop datasets while reducing retrieval costs.

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base (D19-1)

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Tao Shen, Xiubo Geng, Tao Qin, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, Daxin Jiang

Reasoning Over Semantic-Level Graph for Fact Checking (2020.acl-main)

Challenge:	Recent approaches to handle large knowledge base decompose tasks into subtasks and solve them sequentially.
Approach:	They propose a multi-task learning framework that resolves coreference in conversations . they propose enabling shared supervisions and type-aware entity detection model .
Outcome:	The proposed framework improves overall F1 score from 67% to 79% on a large-scale conversational question answering dataset.

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Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan Xu, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Reasoning

Challenge:	Existing methods for fact checking use string concatenation or fusing features of isolated evidence sentences.
Approach:	They propose a method suitable for reasoning about the semantic-level structure of evidence . they use graph convolutional network and graph attention network to exploit the structure .
Outcome:	The proposed method improves claim verification accuracy and FEVER score on a benchmark dataset.

FeTaQA: Free-form Table Question Answering (2022.tacl-1)

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Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, Dragomir Radev

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)

Challenge:	Existing table-based question answering datasets lack advanced information-based questions that require reasoning and integration of information pieces retrieved from structured knowledge sources.
Approach:	They propose a dataset with 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs that can be used to generate an answer.
Outcome:	The proposed dataset has 10K Wikipedia-based table, question, free-form answer, supporting table cells pairs.

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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou

RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations (2023.acl-long)

Challenge:	Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected.
Approach:	They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models.
Outcome:	The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models.

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Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, Dragomir Radev

CogniBench: A Legal-inspired Framework and Dataset for Assessing Cognitive Faithfulness of Large Language Models (2025.acl-long)

Challenge:	Existing Table QA models are vulnerable to task-specific perturbations, such as replacing key question entities or shuffling table columns.
Approach:	They propose to use large language models to generate adversarial examples to enhance training, which significantly improves the robustness of Table QA models.
Outcome:	The proposed model significantly improves on existing Table QA models against human-annotated adversarial perturbations.

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Xiaqiang Tang, Jian Li, Keyu Hu, Nan Du, Xiaolong Li, Xi Zhang, Weigao Sun, Sihong Xie

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering (2025.findings-emnlp)

Challenge:	Existing benchmarks focus on “factual statements” that rephrase source materials, but ignore “cognitive statements” . evaluating and detecting "faithfulness hallucinations" remains challenging .
Approach:	They propose a framework to assess faithfulness of cognitive statements and introduce a dataset to scale easily across models.
Outcome:	The proposed framework assesses faithfulness of cognitive statements and scales easily across models.

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Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen

Structured Data in NLP

Challenge:	Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing to retrieve knowledge strictly necessary for answer generation.
Approach:	They propose a retrieval-filtering-summarization pipeline that enhances QA coverage by retrieving a broader subgraph likely to contain relevant information.
Outcome:	The proposed pipeline surpasses state-of-the-art solutions by about 7% in quality and exceeds GPT-4o (Tool) by 10-21%.

PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training (2022.emnlp-main)

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Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, Xiaoyong Du

Ethical NLP

Challenge:	Table-based fact verification has attracted a lot of attention recently due to the lack of datasets that can be used to pre-train language models to be aware of common table operations.
Approach:	They propose a table-based fact verification tool that pre-trains language models to be aware of common table operations such as aggregating a column or comparing tuples.
Outcome:	The proposed method outperforms previous methods on two table-based fact verification datasets TabFact and SEM-TAB- FACTS.

CodeBERT: A Pre-Trained Model for Programming and Natural Languages (2020.findings-emnlp)

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Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou

Semantic Parsing with Syntax- and Table-Aware SQL Generation (P18-1)

Challenge:	Large pre-trained models have improved performance on a variety of natural language processing tasks.
Approach:	They develop a bimodal pre-trained model for programming language (PL) and natural language (NL) it incorporates a hybrid objective function that detects replaced tokens from generators.
Outcome:	The proposed model performs better on two NL-PL applications by fine-tuning model parameters.

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Yibo Sun, Duyu Tang, Nan Duan, Jianshu Ji, Guihong Cao, Xiaocheng Feng, Bing Qin, Ting Liu, Ming Zhou

Investigating Table-to-Text Generation Capabilities of Large Language Models in Real-World Information Seeking Scenarios (2023.emnlp-industry)

Challenge:	Existing approaches generate a SQL query word-by-word but results are incorrect or not executable due to mismatch between question words and table contents.
Approach:	They propose a generative model to map natural language questions into SQL queries.
Outcome:	The proposed model significantly improves state-of-the-art execution accuracy from 69.0% to 74.4% on a large question- SQL dataset.

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Yilun Zhao, Haowei Zhang, Shengyun Si, Linyong Nan, Xiangru Tang, Arman Cohan

MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering (2025.emnlp-main)

Challenge:	Existing table-to-text generation techniques that transform complex tabular data into comprehensible narratives are lacking in real-world applications.
Approach:	They investigate the table-to-text capabilities of different LLMs using four datasets within two real-world information seeking scenarios.
Outcome:	The proposed models can generate table-to-text data in two real-world information seeking scenarios and perform better than existing models.

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Teng Lin, Yuyu Luo, Honglin Zhang, Jicheng Zhang, Chunlin Liu, Kaishun Wu, Nan Tang

Question Answering

Challenge:	Large Language Models (LLMs) and Retrieval-augmented Generation (RAG) systems show promise, but their performance on cross-document MEQA remains underexplored due to the lack of tailored benchmarks.
Approach:	They propose a scalable multi-document, multi-entity benchmark to evaluate LLMs' capacity to retrieve, consolidate, and reason over scattered and dense information.
Outcome:	The proposed benchmarks show that even advanced models achieve only 59% accuracy on MEBench.

Syntax-Enhanced Pre-trained Model (2021.acl-long)

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Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan

Syntactic Text Processing

Challenge:	Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages.
Approach:	They propose a model that utilizes the syntactic structure of text in pre-training and fine-tuning stages.
Outcome:	The proposed model achieves state-of-the-art on six public benchmark datasets.

SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)

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Nan He, Weichen Xiong, Hanwen Liu, Yi Liao, Lei Ding, Kai Zhang, Guohua Tang, Xiao Han, Yang Wei

Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing (P19-1)

Challenge:	Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication.
Approach:	They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness.
Outcome:	The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%.

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Daya Guo, Duyu Tang, Nan Duan, Ming Zhou, Jian Yin

ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering (2024.findings-emnlp)

Challenge:	a context-aware retrieval model and a meta-learning paradigm are used for context-dependent semantic parsing .
Approach:	They propose a retrieval model and a meta-learner to incorporate retrieved datapoints as context-dependent semantic parsing evidence.
Outcome:	The proposed approach performs better than retrieve-and-edit baselines on CONCODE and CSQA datasets.

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Yifan Wu, Lutao Yan, Leixian Shen, Yunhai Wang, Nan Tang, Yuyu Luo

Analytical Reasoning of Text (2022.findings-naacl)

Challenge:	Chart question answering (ChartQA) tasks are a critical part of visualization charts.
Approach:	They propose a chart question answering task that uses MLLMs to analyze charts . they propose 'Chain-of-Charts' textual prompt strategy that directs attention to visual elements .
Outcome:	The proposed model improves performance by 14.41% and 80% in low-level ChartQA tasks.

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Wanjun Zhong, Siyuan Wang, Duyu Tang, Zenan Xu, Daya Guo, Yining Chen, Jiahai Wang, Jian Yin, Ming Zhou, Nan Duan

Reasoning

Challenge:	Existing models with implicit reasoning ability struggle to solve analytical reasoning of text.
Approach:	They propose an approach to analyze text and use it to perform reasoning over it.
Outcome:	The proposed approach outperforms pre-trained models on an analysis of the Law School Admission Test dataset.

When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models (2024.findings-emnlp)

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Weilan Wang, Yu Mao, Tang Dongdong, Du Hongchao, Nan Guan, Chun Jason Xue

Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)

Challenge:	Large language models (LLMs) exhibit excellent performance in various tasks, but memory requirements present a challenge when deploying on memory-limited devices.
Approach:	They propose a framework to compress LLM after quantization further, achieving about 2.2x compression ratio.
Outcome:	The proposed model can achieve 40% reduction in memory size with negligible loss in accuracy and inference speed.

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Wanjun Zhong, Duyu Tang, Zenan Xu, Ruize Wang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin

Structured Data in NLP

Challenge:	Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents.
Approach:	They propose a graph-based model that captures factual structures of documents for deepfake detection.
Outcome:	The proposed model improves strong base models built with RoBERTa on two public deepfake datasets.

Towards Robust Temporal Activity Localization Learning with Noisy Labels (2024.lrec-main)

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Daizong Liu, Xiaoye Qu, Xiang Fang, Jianfeng Dong, Pan Zhou, Guoshun Nan, Keke Tang, Wanlong Fang, Yu Cheng

Robustness in NLP

Challenge:	Existing methods for temporal activity localization are expensive and difficult to satisfy due to subjective labeling.
Approach:	They propose a new TAL setting where a TAL model should be robust to mixed training data with noisy moment boundaries.
Outcome:	The proposed method is significantly more robust to noisy training data than existing methods.

LogicalFactChecker: Leveraging Logical Operations for Fact Checking with Graph Module Network (2020.acl-main)

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Wanjun Zhong, Duyu Tang, Zhangyin Feng, Nan Duan, Ming Zhou, Ming Gong, Linjun Shou, Daxin Jiang, Jiahai Wang, Jian Yin

Compare to The Knowledge: Graph Neural Fake News Detection with External Knowledge (2021.acl-long)

Challenge:	Existing methods for fact checking textual statements are not yet available.
Approach:	They propose a neural network approach capable of leveraging logical operations for fact checking . they use a textual statement and semi-structured tables to generate a program from it .
Outcome:	The proposed approach achieves state-of-the-art performance on TABFACT dataset . it derives a program (a.k.a. logical form) of the statement in semantic parsing manner .

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Linmei Hu, Tianchi Yang, Luhao Zhang, Wanjun Zhong, Duyu Tang, Chuan Shi, Nan Duan, Ming Zhou

Ethical NLP

Challenge:	Existing methods for fake news detection rely on linguistic and semantic features from news content and do not exploit external knowledge.
Approach:	They propose a graph neural model which compares news to knowledge base through entities for fake news detection.
Outcome:	The proposed model significantly outperforms state-of-the-art methods on two benchmark datasets.

Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image Retrieval (2026.findings-acl)

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Nan Sun, Jing Tang, Lei Sun, Rui Chen, Yuxing Lu, Xiangxiang Chu, Hefei Ling, Yujun Cai

Learning to Collaborate for Question Answering and Asking (N18-1)

Challenge:	Existing methods to retrieve target images suffer from inherent cognitive bias due to unknown candidate distribution.
Approach:	They propose a training-free framework that reframes ZS-CIR as a self-correcting process . they propose to use retrieved results as feedback to perceive the candidate distribution .
Outcome:	Experiments on public benchmarks show that CoRR outperforms other SOTA methods.

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Duyu Tang, Nan Duan, Zhao Yan, Zhirui Zhang, Yibo Sun, Shujie Liu, Yuanhua Lv, Ming Zhou

Natural Language Interfaces

Challenge:	Question answering (QA) and question generation (QG) are closely related tasks.
Approach:	They propose a training algorithm that generalizes both Generative Adversarial Network and Generating Domain-Adaptive Nets under the question answering scenario.
Outcome:	The proposed training algorithm generalizes both Generative Adversarial Network (GAN) and Generating Domain-Adaptive Nets (GDAN) under the question answering scenario.

MAR: Matching-Augmented Reasoning for Enhancing Visual-based Entity Question Answering (2024.emnlp-main)

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Zhengxuan Zhang, Yin Wu, Yuyu Luo, Nan Tang

Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)

Challenge:	Multimodal large language models (MLLMs) struggle with visual-based entity questions (VEQA) MLLM can identify A, but may refrain from answering due to privacy concerns.
Approach:	They propose a method that uses vector representations to analyze visual-based entity questions (VEQA) they use visual cues and textual information to integrate visual cus and visual information .
Outcome:	The proposed method significantly improves visual-based entity question answering (VEQA) it can identify faces, names, and alignments within visual objects, and then derive the answer over this matching graph.

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Ruize Wang, Duyu Tang, Nan Duan, Wanjun Zhong, Zhongyu Wei, Xuanjing Huang, Daxin Jiang, Ming Zhou

Challenge:	Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments.
Approach:	They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts.
Outcome:	The proposed method achieves superior performance on a large dataset for propaganda detection.

QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)

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Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan