Papers by Zhiguo Wang

XSemPLR: Cross-Lingual Semantic Parsing in Multiple Natural Languages and Meaning Representations (2023.acl-long)

Challenge:	Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data.
Approach:	They propose a framework that imposes strong typing constraints and incorporates key relationships from schema.
Outcome:	The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider.

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Yusen Zhang, Jun Wang, Zhiguo Wang, Rui Zhang

SQL-to-Text Generation with Graph-to-Sequence Model (D18-1)

Challenge:	Existing models for cross-lingual semantic parsing are not able to perform tasks on a wide range of datasets.
Approach:	They propose a benchmark for cross-lingual semantic parsing that uses 22 natural languages and 8 meaning representations to translate queries into MRs.
Outcome:	The proposed benchmarks cover 22 natural languages and 8 meaning representations on 164 domains and 5 tasks covering a wide range of multilingual language models.

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Kun Xu, Lingfei Wu, Zhiguo Wang, Yansong Feng, Vadim Sheinin

Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction (2021.acl-long)

Challenge:	Existing approaches to generate SQL-to-text using seq2seq models do not capture graph-structured information in SQL query.
Approach:	They propose a graph-to-sequence model to encode global structure information into node embeddings.
Outcome:	The proposed model outperforms the Seq2Seq and Tree2Sq baselines on the WikiSQL and Stackoverflow datasets.

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Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang

Natural Language Interfaces

Challenge:	Open-domain question answering is a task to answer questions using passages with diverse topics.
Approach:	They propose a model that aggregates evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions.
Outcome:	The proposed model achieves state-of-the-art performance on AmbigQA dataset and shows competitive performance on NQ-Open and TriviaQA.

Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering (2021.eacl-main)

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Zhiguo Wang, Patrick Ng, Ramesh Nallapati, Bing Xiang

You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL (2025.naacl-long)

Challenge:	Existing work on question answering over knowledge bases limited the search space to a subset of KBs . a retrieval-and-rerank framework is used to access KB and rerank retrieved candidates with more powerful neural networks.
Approach:	They propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer.
Outcome:	The proposed method improves accuracy and accuracy on the SimpleQuestions dataset and the FreebaseQA dataset.

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Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng

Entity-level Factual Consistency of Abstractive Text Summarization (2021.eacl-main)

Challenge:	Existing text-to-SQL systems encode the same schema for every question, resulting in unnecessary high inference cost and missing crucial database knowledge.
Approach:	They propose a paradigm that directly internalizes database knowledge into the parametric knowledge of a text-to-SQL model during training and eliminates the need for schema encoding during inference.
Outcome:	The proposed paradigm significantly reduces the input token length by 66%-98% and outperforms traditional systems on three benchmarks.

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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang

Text Generation

Challenge:	Existing models exhibit entity hallucination, generating names of entities that are not present in the source document.
Approach:	They propose to use entity-level factual consistency to improve model quality . they propose to filter the training data to reduce entity hallucination problem .
Outcome:	The proposed model can reduce the entity hallucination problem by filtering the training data.

BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation (2025.findings-acl)

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Jilong Li, Zhenxi Song, Jiaqi Wang, Meishan Zhang, Honghai Liu, Min Zhang, Zhiguo Zhang

Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)

Challenge:	Current EEG/MEG-to-text decoding systems rely on teacher-forcing methods . pre-trained large language models are over-dominant in decoding text from brain activity .
Approach:	They propose a framework that employs decoupled representation learning to achieve state-of-the-art performance on EEG and MEG datasets.
Outcome:	The proposed framework achieves state-of-the-art performance on EEG and MEG datasets.

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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng

Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)

Challenge:	Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality.
Approach:	They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM .
Outcome:	The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL.

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Alexander Fabbri, Patrick Ng, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

Low-Resource NLP

Challenge:	Question Answering (QA) is a field of increasing demand due to the availability of information online.
Approach:	They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence.
Outcome:	The proposed approach improves the performance of a QA model on generated pseudo-training data.

A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)

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Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023.acl-long)

Challenge:	Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph.
Approach:	They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure.
Outcome:	The proposed model outperforms existing methods on a benchmark.

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Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Yang Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

On Synthetic Data Strategies for Domain-Specific Generative Retrieval (2025.acl-long)

Challenge:	Existing methods for data-to-text generation focus on specific types of structured data.
Approach:	They propose a method that provides a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations.
Outcome:	The proposed method improves zero-shot and few-shot scenarios and can adapt to new structured data.

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Haoyang Wen, Jiang Guo, Yi Zhang, Jiarong Jiang, Zhiguo Wang

Information Retrieval

Challenge:	Generative retrieval models can be used to generate ranked lists of potentially relevant document identifiers for a user query.
Approach:	They propose a synthetic data generation strategy for a two-stage training framework that focuses on learning to decode document identifiers from queries and a strategy for mining hard negatives based on initial model's predictions.
Outcome:	The proposed model can generate ranked lists of potentially relevant document identifiers for a user query and then refine ranking through preference learning.

Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering (2021.acl-long)

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Alexander Hanbo Li, Patrick Ng, Peng Xu, Henghui Zhu, Zhiguo Wang, Bing Xiang

Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering (N19-1)

Challenge:	Existing generative models for open-domain question answering focus on generating direct answers from unstructured textual information, but a large amount of knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.
Approach:	They propose a hybrid framework that takes both textual and tabular evidences as input and generates either direct answers or SQL queries depending on which form could better answer the question.
Outcome:	The proposed framework outperforms baseline models on OpenSQuAD datasets and can generate SQL queries on the associated databases to obtain the final answers.

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Kun Xu, Yuxuan Lai, Yansong Feng, Zhiguo Wang

Natural Language Interfaces

Challenge:	Existing Key-value Memory Neural Networks are effective for shallow reasoning over documents . but extending them to Knowledge Based Question Answering is not trivial .
Approach:	They propose a mechanism to enable conventional KV-MemNNs models to perform interpretable reasoning for complex questions.
Outcome:	The proposed solution provides better reasoning abilities on complex questions and achieves state-of-the-art performance.

Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)

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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta

Leveraging Context Information for Natural Question Generation (N18-2)

Challenge:	Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query.
Approach:	They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding.
Outcome:	The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set.

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Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea

Text Generation

Challenge:	Existing work for natural question generation ignores the input passage or hard-codes answer positions.
Approach:	They propose a model that matches the answer with the passage before generating a question.
Outcome:	The proposed model outperforms the state-of-the-art model using rich features.

Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks (2024.findings-acl)

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Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang

Reasoning

Challenge:	Existing knowledge editing methods struggle to effectively propagate updates to interconnected facts, limiting the performance of reasoning tasks based on these updated facts.
Approach:	They propose a reasoning-based benchmark, ReCoE, which covers six common reasoning schemes in the real world.
Outcome:	The proposed reasoning-based benchmark shows that current models struggle to propagate updated knowledge within reasoning schemes.

Improving Factual Consistency of Abstractive Summarization via Question Answering (2021.acl-long)

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Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang

Summarization

Challenge:	Recent studies show that about 30% of summaries generated by neural text summarization suffer from fact fabrication.
Approach:	They propose an automatic evaluation metric to measure factual consistency and a learning algorithm that maximizes the metric during model training.
Outcome:	The proposed method improves factual consistency and overall quality of summarization models.

PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries (2025.naacl-long)

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Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu, Anuj Chauhan, Chung-Wei Hang, Shuaichen Chang, Lin Pan, Wuwei Lan, Henghui Zhu, Jiarong Jiang, Patrick Ng, Zhiguo Wang

Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model (D18-1)

Challenge:	Existing text-to-SQL systems focus on user questions with clear intentions that can be answered, but real user questions can be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data.
Approach:	They construct a conversational text-to-SQL dataset called PRACTIQ, consisting of ambiguous and unanswerable questions inspired by real-world user questions.
Outcome:	The proposed system generates conversations with four turns, generating the user’s question, an assistant response seeking clarification, and the user's clarified SQL response with the natural language explanation of the execution results.

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Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Liwei Chen, Vadim Sheinin

Domain Adaptation with BERT-based Domain Classification and Data Selection (D19-61)

Challenge:	Existing neural semantic parsers extract word order features while neglecting other valuable syntactic information.
Approach:	They propose to use syntactic graph to represent three types of syntaktic information . they then employ a graph-to-sequence model to encode the syntastic graph and decode a logical form .
Outcome:	The proposed model is comparable to the state-of-the-art on Jobs640, ATIS, and Geo880.

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Xiaofei Ma, Peng Xu, Zhiguo Wang, Ramesh Nallapati, Bing Xiang

Language Models

Challenge:	Modern deep neural models with millions of parameters can easily adapt to a new learning task and dataset when enough supervision is given.
Approach:	They propose a domain adaptation framework based on curriculum learning and domain-discriminative data selection.
Outcome:	The proposed framework outperforms discrepancy-based methods on transfer tasks while consuming only fraction of training budget.

CiteEval: Principle-Driven Citation Evaluation for Source Attribution (2025.acl-long)

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Yumo Xu, Peng Qi, Jifan Chen, Kunlun Liu, Rujun Han, Lan Liu, Bonan Min, Vittorio Castelli, Arshit Gupta, Zhiguo Wang

Challenge:	Current evaluation frameworks rely on NLI to assess binary or ternary support from cited sources, which is suboptimal for citation evaluation.
Approach:	They propose a citation evaluation framework based on fine-grained citation ratings within a broad context and construct a multi-domain benchmark with high-quality human annotations.
Outcome:	The proposed framework provides a high-quality human annotation benchmark and a suite of model-based metrics that exhibit strong correlation with human judgments.

Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (D19-1)

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Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, Bing Xiang

Natural Language Interfaces

Challenge:	Existing studies have shown that BERT models can find answers from multiple passages . however, the results of these studies are still unaddressed.
Approach:	They propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question.
Outcome:	The proposed model outperforms state-of-the-art models on four benchmarks.

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network (P19-1)

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Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, Dong Yu

Structured Data in NLP

Challenge:	Existing approaches to cross-lingual knowledge graph (KG) alignment rely on entity embeddings derived from monolingual KG structural information.
Approach:	They propose a topic entity graph to represent entities with contextual information in KGs.
Outcome:	The proposed model outperforms state-of-the-art methods by a large margin.

Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge (2023.findings-acl)

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Xingyu Fu, Sheng Zhang, Gukyeong Kwon, Pramuditha Perera, Henghui Zhu, Yuhao Zhang, Alexander Hanbo Li, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Patrick Ng, Dan Roth, Bing Xiang

N-ary Relation Extraction using Graph-State LSTM (D18-1)

Challenge:	Open-ended Visual Question Answering (VQA) requires models to reason over visual and natural language inputs using world knowledge.
Approach:	They propose a new VQA pipeline that deploys a generate-then-select strategy guided by world knowledge for the first time.
Outcome:	The proposed pipeline expands the knowledge coverage from in-domain training data by 4.1% on OK-VQA, without additional computation cost.

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Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea

Information Extraction & Text Mining

Challenge:	Existing methods for cross-sentence relation extraction split the input graph into two DAGs, but important information can be lost in the splitting procedure.
Approach:	They propose a graph-state LSTM model which uses a parallel state to model each word, recurrently enriching state values via message passing.
Outcome:	The proposed model keeps the original graph structure, and speeds up computation by allowing more parallelization.

Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder (2024.acl-long)

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Jiaqi Wang, Zhenxi Song, Zhengyu Ma, Xipeng Qiu, Min Zhang, Zhiguo Zhang