Papers by Wai Lam

Partner Personas Generation for Dialogue Response Generation (2022.naacl-main)

Challenge:	Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, and labeled graph.
Approach:	They propose to use existing English parser to learn and improve multilingual AMR parsers . their results show that noisy input and precise output are key to successful distillation .
Outcome:	The proposed model outperforms the current state-of-the-art English-only parser on four different languages.

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Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng

Consecutive Batch Model Editing with HooK Layers (2024.emnlp-main)

Challenge:	Existing frameworks that focus on self personas ignore the value of partner persona . experimental results show that our framework generates relevant, interesting, coherent and informative partner personages even compared to ground truth partner personagers.
Approach:	They propose a framework that leverages automatic partner personas generation to enhance dialogue response generation.
Outcome:	The proposed framework generates relevant, interesting, coherent and informative partner personas even compared to ground truth partner person . it surpasses baselines that condition on ground truth persona .

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Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam

Challenge:	Existing models that retrain are time- and resource-consuming, but they lack the memory to support sequential and batch editing.
Approach:	They propose a model editing method that supports sequential and batch editing . they use a small amount of memory to store several hook layers that remain unchanged over time .
Outcome:	The proposed method is memory-friendly and can store hook layers that remain unchanged over time.

AnswerFact: Fact Checking in Product Question Answering (2020.emnlp-main)

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Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam

Question Answering

Challenge:	a product-related community question answering platform is widely employed in many E-commerce sites . however, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information.
Approach:	They propose a large scale fact checking dataset from product question answering forums to predict the answer veracity . each answer is accompanied by its veraity label and associated evidence sentences .
Outcome:	The proposed model outperforms baselines on the question veracity prediction task.

Enhancing Grammatical Error Correction Systems with Explanations (2023.acl-long)

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Yuejiao Fei, Leyang Cui, Sen Yang, Wai Lam, Zhenzhong Lan, Shuming Shi

UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue (2022.acl-short)

Challenge:	To help language learners better understand why the GEC system makes a correction, the causes of errors and the corresponding error types are two key factors.
Approach:	They propose to annotate large dataset with evidence words and grammatical error types to help language learners better understand corrections.
Outcome:	The proposed model can be validated by human evaluation and can be used to help second-language learners decide whether to accept a correction suggestion and understand the associated grammar rule.

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Chang Gao, Wenxuan Zhang, Wai Lam

Dynamic Topic Tracker for KB-to-Text Generation (2020.coling-main)

Challenge:	Existing studies tackle the problem of error propagation by decomposing the goal-oriented document-grounded dialogue into two sub-tasks.
Approach:	They propose to unify knowledge identification and response generation into two sub-tasks by sequentially generating grounding knowledge and response.
Outcome:	The proposed framework unifies knowledge identification and response generation and models their characteristics using a prompt-connected multi-task learning strategy.

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Zihao Fu, Lidong Bing, Wai Lam, Shoaib Jameel

Knowledge Representation

Challenge:	Existing KB-to-text generation models suffer from an off-topic problem . existing models generate unrelated clauses regardless of input data .
Approach:	They propose a dynamic topic tracker that learns a global hidden representation for topics and recognizes the corresponding topic during each generation step.
Outcome:	The proposed model improves the performance of sentence generation and mitigates off-topic problem.

A Thorough Examination of Decoding Methods in the Era of LLMs (2024.emnlp-main)

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Chufan Shi, Haoran Yang, Deng Cai, Zhisong Zhang, Yifan Wang, Yujiu Yang, Wai Lam

Reasons to Reject? Aligning Language Models with Judgments (2024.findings-acl)

Challenge:	Decoding methods are essential for converting language models from next-token predictors into practical task solvers.
Approach:	They propose to evaluate decoding methods in general-purpose large language models . they find that decoding method performance is notably task-dependent .
Outcome:	The proposed methods perform task-dependently and are influenced by alignment, model size, and quantization.

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Weiwen Xu, Deng Cai, Zhisong Zhang, Wai Lam, Shuming Shi

PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance (2022.emnlp-main)

Challenge:	a new framework for aligning large language models with judgments is proposed to help with alignment . a framework that allows for fine-grained inappropriate content detection and correction based on judgments . large language model alignment is critical for making artificial intelligence a reliable ally for humanity .
Approach:	They propose a framework that allows for fine-grained inappropriate content detection and correction based on judgments.
Outcome:	The proposed framework beats the 175B DaVinci003 and improves on AlpacaEval using judgments.

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Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua

Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation (2024.findings-eacl)

Challenge:	Existing studies on financial question answering systems focus on passively responding to user queries.
Approach:	They propose a new dataset to facilitate conversational question answering over hybrid contexts in finance . they propose PACIFIC to combine clarification question generation and CQA .
Outcome:	The proposed method performs multi-task learning over all sub-tasks in PACIFIC . it incorporates a simple ensemble strategy to alleviate error propagation issue .

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Hongyuan Lu, Haoyang Huang, Dongdong Zhang, Furu Wei, Wai Lam

Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations (2023.acl-long)

Challenge:	Current multilingual agreement (MA) methods require parallel data between multiple language pairs, which is not always realistic and optimize the agreement in an ambiguous direction, which hampers the translation performance.
Approach:	They propose a novel multilingual agreement framework that optimizes agreement bidirectionally with the Kullback-Leibler Divergence loss.
Outcome:	The proposed method improves strong baselines on the task of multilingual neural machine translation with three benchmarks: TED Talks, News, and Europarl.

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Yang Deng, Wenxuan Zhang, Yifei Yuan, Wai Lam

Unsupervised KB-to-Text Generation with Auxiliary Triple Extraction using Dual Learning (2020.aacl-main)

Challenge:	Experimental results show the superiority of a mixed-initiative framework for emotional support conversation (ESC) ESC systems are emerging to provide prompt and convenient emotional support for helpseekers, including mental health support, counseling or motivational interviewing.
Approach:	They propose a knowledge-enhanced mixed-initiative framework that retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses.
Outcome:	The proposed framework retrieves actual case knowledge from a large-scale mental health knowledge graph for generating mixed-initiative responses.

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Zihao Fu, Bei Shi, Lidong Bing, Wai Lam

Low-Resource NLP

Challenge:	Existing methods to generate text from KB triples are limited and expensive . a novel approach is proposed to train the generation model in unsupervised way .
Approach:	They propose a method which trains the generation model in a completely unsupervised way with unaligned raw text data and KB triples.
Outcome:	The proposed method outperforms existing methods and is cost-effective.

Aspect-based Sentiment Analysis in Question Answering Forums (2021.findings-emnlp)

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Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam

Once Upon a Time in Graph: Relative-Time Pretraining for Complex Temporal Reasoning (2023.emnlp-main)

Challenge:	Existing studies on aspects-based sentiment analysis focus on a single opinionated sentence.
Approach:	They propose a model to combine aspects and their sentiments for QA forums . they use cross-sentence aspect-opinion interaction modeling to align the aspect mentioned in the question and associated opinion clues in the answer.
Outcome:	The proposed model outperforms baseline models on three real-world datasets.

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Sen Yang, Xin Li, Lidong Bing, Wai Lam

Multimodality

Challenge:	Existing work focuses on strengthening the knowledge-time association between text and time-stamps, but this is insufficient for downstream tasks.
Approach:	They propose a model that explicitly connects all temporally-scoped facts by modeling the time relations between any two sentences.
Outcome:	The proposed model outperforms baseline T5 on multiple temporal question answering datasets . it is especially good at modeling long-range complex temporal dependencies, the authors say .

PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation (2023.eacl-main)

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Hongyuan Lu, Wai Lam

Paraphrasing

Challenge:	Curriculum Data Augmentation (CDA) presents synthetic data with increasing difficulties to neural models.
Approach:	They propose a curriculum-aware paraphrase generation module with bottom-k sampling and cyclic learning strategy that passes through the curriculums multiple times.
Outcome:	The proposed framework surpasses competitive baselines on few-shot text classification and dialogue generation.

LLM2: Let Large Language Models Harness System 2 Reasoning (2025.naacl-short)

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Cheng Yang, Chufan Shi, Siheng Li, Bo Shui, Yujiu Yang, Wai Lam

Multi-LLM Collaborative Search for Complex Problem Solving (2026.findings-acl)

Challenge:	Empirical results on mathematical reasoning benchmarks substantiate the efficacy of Large language models (LLMs).
Approach:	They propose a framework that combines an LLM with a process-based verifier to generate plausible candidates and provide timely process-driven feedback to distinguish desirable and undesirable outputs.
Outcome:	Empirical results show that LLM2 improves accuracy on GSM8K and self-consistency increases major@20 accuracy.

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Sen Yang, Yafu Li, Wai Lam, Yu Cheng

Reasoning

Challenge:	Large language models (LLMs) often struggle with complex reasoning tasks due to the vast reasoning space inherent in the complexity and inherent ambiguities of natural languages.
Approach:	They propose a mixture-of-search-agents paradigm that integrates diverse reasoning pathways by combining independent exploration and iterative refinement among multiple LLMs.
Outcome:	The proposed approach improves performance over single-agent and multi-agend baselines in complex mathematical and commonsense reasoning tasks.

Unveiling the Generalization Power of Fine-Tuned Large Language Models (2024.naacl-long)

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Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu, Pheng-Ann Heng, Wai Lam

SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, but the comprehensive effects of fine-tuning on the LLMs’ generalization ability are not fully understood.
Approach:	They conduct extensive experiments across five distinct language tasks on different datasets to investigate whether fine-tuning affects the generalization ability intrinsic to LLMs.
Outcome:	The proposed model can generalize to different domains and tasks by integrating the in-context learning strategy during fine-tuning on generation tasks.

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Chengxing Xie, Bowen Li, Chang Gao, He Du, Wai Lam, Difan Zou, Kai Chen

Green & Sustainable NLP

Challenge:	Large Language Models excel in code generation benchmarks, but these benchmarks focus on single-file scenarios with constrained context scope.
Approach:	They propose an open-source framework to effectively resolve GitHub issues using a code file retrieval module and a model-based code editing module.
Outcome:	The proposed approach achieves state-of-the-art performance on two GitHub benchmarks.

TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets (2023.findings-emnlp)

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Hongyuan Lu, Haoyang Huang, Shuming Ma, Dongdong Zhang, Wai Lam, Zhaochuan Gao, Anthony Aue, Arul Menezes, Furu Wei

Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation (2021.findings-emnlp)

Challenge:	Existing approaches to multilingual sequence-to-sequence pre-training rely on monolingual corpora and sometimes synthetic document-level bilingual corporata.
Approach:	They propose to leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training by using a novel method called Grafting.
Outcome:	The proposed method achieves strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark.

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Haoran Yang, Wai Lam, Piji Li

Representation Learning

Challenge:	Exemplar-Guided Paraphrase Generation (EGPG) aims to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence.
Approach:	They propose a method to generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence.
Outcome:	The proposed method can generate a target sentence which conforms to the style of the given exemplar while encapsulating the content information of the source sentence.

DepWiGNN: A Depth-wise Graph Neural Network for Multi-hop Spatial Reasoning in Text (2023.findings-emnlp)

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Shuaiyi Li, Yang Deng, Wai Lam

Structured Data in NLP

Challenge:	Existing approaches for spatial reasoning in text overlook the gap between natural language and symbolic structures.
Approach:	They propose a novel depth-wise Graph Neural Network to aggregate spatial information over the depth dimension instead of the breadth dimension of the graph.
Outcome:	The proposed model outperforms existing methods on two multi-hop spatial reasoning datasets.

Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion (D19-1)

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Zihao Wang, Kwunping Lai, Piji Li, Lidong Bing, Wai Lam

A Frustratingly Simple Decoding Method for Neural Text Generation (2024.lrec-main)

Challenge:	Recent studies have focused on the large proportion of infrequent relations which have been ignored by previous studies.
Approach:	They propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions.
Outcome:	The proposed framework outperforms existing methods when dealing with infrequent relations and uncommon entities.

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Haoran Yang, Deng Cai, Huayang Li, Wei Bi, Wai Lam, Shuming Shi

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Neural text generation is notorious for repetitive loops and tedious outputs.
Approach:	They propose a method that penalizes future generation of repetitive content . they construct an anti-LM based on previously generated text .
Outcome:	The proposed method outperforms established baselines in terms of generation quality, decoding speed, and universality.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Easy-to-Hard Learning for Information Extraction (2023.findings-acl)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Chang Gao, Wenxuan Zhang, Wai Lam, Lidong Bing

Information Extraction & Text Mining

Challenge:	Existing models for information extraction (IE) use a one-stage learning strategy to extract the target structure from unstructured text data.
Approach:	They propose a unified easy-to-hard learning framework that mimics the human learning process by breaking down the learning process into multiple stages.
Outcome:	The proposed framework enables the model to acquire general IE task knowledge and improve its generalization ability on 13 out of 17 datasets.

Review-based Question Generation with Adaptive Instance Transfer and Augmentation (2020.acl-main)

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Qian Yu, Lidong Bing, Qiong Zhang, Wai Lam, Luo Si

Text Generation

Challenge:	Existing methods to generate questions for verbose reviews are inefficient for potential consumers . lack of training data hinders efficient review digestion, authors say .
Approach:	They propose to generate questions that can be answered by corresponding review sentences . they propose an iterative learning framework with adaptive instance transfer and augmentation .
Outcome:	The proposed model can generate questions that can be answered by review sentences . it is easier to find critical review parts that are important for potential consumers .

AMR Parsing via Graph-Sequence Iterative Inference (2020.acl-main)

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Deng Cai, Wai Lam

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

Challenge:	Abstract Meaning Representation (AMR) parsing is a broad-coverage semantic formalism that encodes the meaning of a sentence as a rooted, directed, labeled graph.
Approach:	They propose a model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph.
Outcome:	The proposed model outperforms existing models by large margins on both input sequence and output graph.

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

LNE-Blocking: An Efficient Framework for Contamination Mitigation Evaluation on Large Language Models (2025.findings-emnlp)

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

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Ruijie Hou, Yueyang Jiao, Hanxu Hu, Yingming Li, Wai Lam, Huajian Zhang, Hongyuan Lu

ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (2022.emnlp-main)

Challenge:	a problem of data contamination is now almost inevitable during the development of large language models, with the training data often integrating evaluation benchmarks even unintentionally.
Approach:	They propose a framework to restore model performance prior to data contamination on potentially leaked datasets by using contamination detection and disruption operation.
Outcome:	The proposed framework restores model performance prior to contamination on potentially leaked datasets.

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Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

Information Extraction & Text Mining

Challenge:	Existing CCE methods treat contracts as plain text, creating a barrier to understanding complex contracts.
Approach:	They propose a framework to model implicit relations in legal contracts to improve contract understanding . they propose Term-Definition Relation captures the relation between important terms and their definitions .
Outcome:	The proposed framework improves on two CCE tasks in conventional and zero-shot settings.

The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)

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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam

Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection (2020.coling-main)

Challenge:	Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others.
Approach:	They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM.
Outcome:	The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants.

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Yang Deng, Wenxuan Zhang, Wai Lam

Challenge:	Existing methods for multi-turn response selection are not practical as the turns of conversations vary.
Approach:	They propose to use latent interaction modeling to model multi-level interactions between utterance and response.
Outcome:	The proposed method outperforms state-of-the-art methods on three multi-turn response selection benchmark datasets.

Answering Product-related Questions with Heterogeneous Information (2020.aacl-main)

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Wenxuan Zhang, Qian Yu, Wai Lam

Exploring Compositional Generalization of Large Language Models (2024.naacl-srw)

Challenge:	Existing product question answering methods only consider a single information source such as user reviews and/or require large amounts of labeled data.
Approach:	They propose a framework to exploit heterogeneous information including natural language text and attribute-value pairs from two information sources of the concerned product, namely product details and user reviews.
Outcome:	The proposed framework achieves superior performance over state-of-the-art models on a real-world dataset.

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Haoran Yang, Hongyuan Lu, Wai Lam, Deng Cai

McQueen: a Benchmark for Multimodal Conversational Query Rewrite (2022.emnlp-main)

Challenge:	a recent study has found that large language models can generalize compositional instructions from simple instructions to complex ones.
Approach:	They study the generalization ability of large language models with respect to compositional instructions . they first construct a dataset with the help of ChatGPT guided by the self-instruct technique .
Outcome:	The proposed model can generalize from simple instructions to more intricate ones, the authors show . their results show that training LLMs on higher-order compositional instructions improves performance on lower-order ones, but not on higher order ones.

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Yifei Yuan, Chen Shi, Runze Wang, Liyi Chen, Feijun Jiang, Yuan You, Wai Lam

Adam’s Law: Textual Frequency Law on Large Language Models (2026.acl-long)

Challenge:	Recent studies have focused on conversational-related tasks that involve drawing information from more than one modality.
Approach:	They propose a task of multimodal conversational query rewrite which performs query . they collect a large-scale visual conversation dataset and benchmark it against other tasks .
Outcome:	The proposed task performs on a large-scale visual conversation dataset . it eliminates coreference and ellipsis in the original query without changing its semantic information.

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Hongyuan Lu, Zixuan Li, Zefan Zhang, Bowen Cao, Wai Lam

Multi-hop Inference for Question-driven Summarization (2020.emnlp-main)

Challenge:	Textual frequency is a topic of understudied research, but its relevance to Large Language Models is not well understood.
Approach:	They propose a framework to estimate textual data frequency using a paraphraser and a textual distillation method to refine LLMs.
Outcome:	The proposed framework can be used to estimate sentence-level frequency with word-level frequencies.

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Yang Deng, Wenxuan Zhang, Wai Lam

Summarization

Challenge:	Existing methods for summarizing source document for non-factoid questions are lacking in factoidic QA.
Approach:	They propose a question-driven abstractive summarization method that incorporates multi-hop reasoning into question-based summarizing.
Outcome:	The proposed method outperforms state-of-the-art methods on two non-factoid QA datasets.

Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-Switching (2021.emnlp-main)

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Wenxuan Zhang, Ruidan He, Haiyun Peng, Lidong Bing, Wai Lam

Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory (N19-1)

Challenge:	Existing studies on Aspect-based sentiment analysis (ABSA) focus on English texts, but handling it in resource-poor languages remains a challenge.
Approach:	They propose an unsupervised cross-lingual transfer method for the Aspect-based sentiment analysis task . they propose an aspect code-switching mechanism to augment training data with code-linked bilingual sentences .
Outcome:	The proposed method preserves task-specific knowledge in the target language.

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Deng Cai, Yan Wang, Wei Bi, Zhaopeng Tu, Xiaojiang Liu, Wai Lam, Shuming Shi

InfiniteICL: Breaking the Limit of Context Window Size via Long Short-term Memory Transformation (2025.findings-acl)

Challenge:	Existing generative dialogue models generate responses from input queries . however, the results are limited and the models are unsatisfactory .
Approach:	They propose a framework which exploits retrieval results via a skeleton-to-response paradigm . they extract a query skelet and use it to generate a new skele and response .
Outcome:	The proposed approach significantly improves the informativeness of the generated responses.

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Bowen Cao, Deng Cai, Wai Lam

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (D19-55)

Challenge:	InfiniteICL is a framework that parallels context and parameters in large language models with short- and long-term memory in human cognitive systems.
Approach:	They propose a framework that parallels context and parameters in large language models with short- and long-term memory in human cognitive systems and enables infinite context integration.
Outcome:	The proposed framework reduces context length by 90% while achieving 103% average performance of full-context prompting across fact recall, grounded reasoning, and skill acquisition tasks.

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Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam

On Controlling Fallback Responses for Grounded Dialogue Generation (2022.findings-acl)

Challenge:	Existing studies on ABSA use a sequence tagging problem to extract aspect-specific opinion words from the sentence given the aspect.
Approach:	They build a series of simple yet insightful neural baselines to deal with E2E-ABSA task using contextualized embeddings from pre-trained language models.
Outcome:	The proposed architecture outperforms state-of-the-art models even with a simple linear classification layer.

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Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng

Neural Machine Translation with Monolingual Translation Memory (2021.acl-long)

Challenge:	Existing knowledge grounded dialogue frameworks assume that the user intention is always answerable.
Approach:	They propose a framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable context.
Outcome:	The proposed framework incorporates fallback responses to respond to unanswerable contexts in an informative manner while retaining informativeness for answerable context.

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Deng Cai, Yan Wang, Huayang Li, Wai Lam, Lemao Liu

mPMR: A Multilingual Pre-trained Machine Reader at Scale (2023.acl-short)

Challenge:	Existing work has shown that Translation Memory (TM) can boost the performance of Neural Machine Translation (NMT)
Approach:	They propose a framework that uses monolingual memory and performs learnable memory retrieval in a cross-lingual manner.
Outcome:	The proposed framework outperforms strong TM-augmented NMT baselines using bilingual TM and outperformed existing models in low-resource and domain adaptation scenarios.

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Weiwen Xu, Xin Li, Wai Lam, Lidong Bing

Information Extraction & Text Mining

Challenge:	Existing mPLMs only transfer NLU capability from source to target languages . mPMR allows direct inheritance of multilingual NLU capabilities to downstream tasks .
Approach:	They propose a method to guide multilingual pre-trained language models to perform natural language understanding in multiple languages.
Outcome:	mPMR enables multilingual pre-trained language models to perform natural language understanding (NLU) in multiple languages.

Rephrasing Invokes Better Generations for Large Language Models (2024.naacl-srw)

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Haoran Yang, Hongyuan Lu, Wai Lam

CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion (2024.findings-acl)

Challenge:	Existing methods for prompt tuning and input pre-processing are under-studied . e.g., ReLLM replaces low-frequency words with their high-frequency counterparts .
Approach:	They propose a method that automatically paraphrases input content for better output generation.
Outcome:	The proposed method is user-friendly and requires no additional training.

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Qibing Ren, Chang Gao, Jing Shao, Junchi Yan, Xin Tan, Wai Lam, Lizhuang Ma

Programming Languages in NLP

Challenge:	Large Language Models exhibit remarkable generative capabilities but can be misused for harmful purposes.
Approach:	They propose a framework that transforms natural language inputs into code inputs.
Outcome:	The proposed framework bypasses the safety guardrails of all models more than 80% of the time.

Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation (2022.emnlp-main)

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Deng Cai, Xin Li, Jackie Chun-Sing Ho, Lidong Bing, Wai Lam

Exploiting Reasoning Chains for Multi-hop Science Question Answering (2021.findings-emnlp)

Challenge:	Existing studies on multilingual sentence embeddings focus on cross-lingual semantic textual similarity and transfer tasks.
Approach:	They propose a method to improve existing multilingual sentence embeddings with Abstract Meaning Representation (AMR) . they compare existing multi-lingual sentence embedded with AMR and improve their versions by reducing the surface variations across different languages and expressions.
Outcome:	The proposed method improves state-of-the-art multilingual sentence embeddings on transfer tasks and semantic textual similarity tests.

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Weiwen Xu, Yang Deng, Huihui Zhang, Deng Cai, Wai Lam

Towards Generalizable and Robust Text-to-SQL Parsing (2022.findings-emnlp)

Challenge:	Existing frameworks for multi-hop Science question answering do not require corpus-specific annotations.
Approach:	They propose a chain-guided retriever-reader framework that performs explainable reasoning without corpus annotations.
Outcome:	The proposed framework performs explainable reasoning without corpus-specific annotations . it is shown to be effective on OpenBookQA and ARC-Challenge .

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Chang Gao, Bowen Li, Wenxuan Zhang, Wai Lam, Binhua Li, Fei Huang, Luo Si, Yongbin Li

Programming Languages in NLP

Challenge:	Text-to-SQL parsers must be generalizable and robust against input perturbations.
Approach:	They propose a novel framework to learn text-to-SQL parsing in stages to improve parser's ability to acquire general SQL knowledge instead of capturing spurious patterns.
Outcome:	The proposed framework achieves state-of-the-art performance on the Spider, SParC, and CoSQL datasets.

Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation (2020.aacl-main)

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Yifei Yuan, Jingbo Zhou, Wai Lam

Question Answering

Challenge:	Existing methods for POI oriented question answering lack ability to handle important POI related information.
Approach:	They propose a deep learning framework integrated with joint inference to capture tag semantic and geographic correlation between question and POIs.
Outcome:	The proposed model captures both tag semantic and geographic correlation between question and POIs.

Partially-Aligned Data-to-Text Generation with Distant Supervision (2020.emnlp-main)

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Zihao Fu, Bei Shi, Wai Lam, Lidong Bing, Zhiyuan Liu

Text Generation

Challenge:	Using partially-aligned data is an alternative way of solving the dataset scarcity problem.
Approach:	They propose a task to generate human-readable text for describing some given structured data enabling more interpretability.
Outcome:	The proposed framework outperforms baseline models and validates the feasibility of using partially-aligned data.

Towards Generative Aspect-Based Sentiment Analysis (2021.acl-short)

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Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam

Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering (2021.findings-acl)

Challenge:	Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA.
Approach:	They propose to tackle various ABSA tasks in a unified generative framework . they propose to use annotation-style and extraction-style modeling to enable training .
Outcome:	The proposed framework achieves state-of-the-art on four ABSA tasks across multiple benchmark datasets.

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Weiwen Xu, Huihui Zhang, Deng Cai, Wai Lam

Multimodality

Challenge:	Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process.
Approach:	They propose a framework to exploit more valid facts while obtaining explainability for multi-hop question answering at web scale by dynamically constructing a semantic graph and reasoning over it.
Outcome:	The proposed framework surpasses existing approaches while maintaining high explainability on OpenBookQA and ARC-Challenge.

Core Semantic First: A Top-down Approach for AMR Parsing (D19-1)

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Deng Cai, Wai Lam

Bridging the Gap between Pre-Training and Fine-Tuning for Commonsense Generation (2023.findings-eacl)

Challenge:	Abstract Meaning Representation (AMR) parsing is a semantic formalism that encodes the meaning of a sentence as a rooted labeled directed graph.
Approach:	They propose a scheme for parsing text into its Abstract Meaning Representation (AMR) using Graph Spanning based Parsing.
Outcome:	The proposed scheme achieves state-of-the-art on the latest AMR sembank and no heuristic graph re-categorization is adopted.

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Haoran Yang, Yan Wang, Piji Li, Wei Bi, Wai Lam, Chen Xu

SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation (2025.findings-acl)

Challenge:	Existing methods focusing on this task usually concatenate the concatened concepts words as the inputs of a pre-trained language model (PLM) however, in pre-training, the input is often corrupted sentences with correct word order.
Approach:	They propose a two-stage framework to improve the ability of pre-trained language models to deal with masked sentences with incorrect word order and a special token to make the input distribution more similar to the one used in pre-training.
Outcome:	The proposed method is able to generate a sentence containing all given concepts and correctly describe the relations between concepts.

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Ting Xu, Zhichao Huang, Jiankai Sun, Shanbo Cheng, Wai Lam

Machine Translation

Challenge:	SeqPO-SiMT is a new policy optimization framework for simultaneous machine translation that combines a tailored reward with a single step task.
Approach:	They propose a new policy optimization framework that defines the simultaneous machine translation task as a sequential decision making problem with a tailored reward.
Outcome:	The proposed framework outperforms the supervised fine-tuning model by 1.13 points while reducing the Average Lagging by 6.17 in the NEWSTEST2021 En Zh dataset.

PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks (2023.acl-long)

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Weiwen Xu, Xin Li, Yang Deng, Wai Lam, Lidong Bing

Chain-of-Dictionary Prompting Elicits Translation in Large Language Models (2024.emnlp-main)

Challenge:	Experimental results on ten datasets across seven domains demonstrate the effectiveness of PeerDA.
Approach:	They propose a new approach which uses span pairs with the PR relation as the augmentation data for training.
Outcome:	The proposed approach achieves state-of-the-art results on ten datasets across seven domains.

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Hongyuan Lu, Haoran Yang, Haoyang Huang, Dongdong Zhang, Wai Lam, Furu Wei

Responding E-commerce Product Questions via Exploiting QA Collections and Reviews (C18-1)

Challenge:	Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation . yet, they struggle with translating low-resource languages.
Approach:	They propose a framework that chained multilingual dictionaries to elicit translation abilities for LLMs . they show that CoD can significantly improve LLM translation by evoking more information .
Outcome:	The proposed framework improves on ChatGPT and InstructGPT's translation abilities.

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Qian Yu, Wai Lam, Zihao Wang

Transformation Networks for Target-Oriented Sentiment Classification (P18-1)

Challenge:	Existing QA and review collections can be used to provide instant responses to product questions . a proposed framework can be applied to a real-world commercial E-commerce site .
Approach:	They propose a framework for automatically responding product questions in E-commerce sites . existing QA pairs are exploited as distant supervision for learning to rank responses .
Outcome:	The proposed framework can return a ranked list of snippets serving as the automated response for a given question.

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Xin Li, Lidong Bing, Wai Lam, Bei Shi

Text Classification

Challenge:	a new model for sentiment classification uses attention instead of attention to classify sentiment polarities over individual opinion targets.
Approach:	They propose a model that uses a CNN layer to extract salient features from transformed word representations from a bi-directional RNN layer.
Outcome:	The proposed model achieves state-of-the-art on a few benchmarks.

Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)

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Bei Shi, Zihao Fu, Lidong Bing, Wai Lam

Information Retrieval

Challenge:	Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words.
Approach:	They propose a method for learning domain-sensitive and sentiment-aware embeddings that captures sentiment semantics and domain sensitivity of individual words.
Outcome:	The proposed method can produce domain-common embeddings and domain-specific embedds.

Fact Discovery from Knowledge Base via Facet Decomposition (N19-1)

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Zihao Fu, Yankai Lin, Zhiyuan Liu, Wai Lam

Knowledge Representation

Challenge:	Recent years have witnessed the emergence and growth of many large-scale knowledge bases (KBs) however, there are some issues unsettled towards enriching the KBs.
Approach:	They propose a framework that decomposes the discovery problem into several facet components and an auto-encoder component to estimate some facets of the fact.
Outcome:	The proposed framework achieves promising results on a benchmark dataset.

Neuro-Symbolic Integration Brings Causal and Reliable Reasoning Proofs (2025.findings-naacl)

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Sen Yang, Xin Li, Leyang Cui, Lidong Bing, Wai Lam

Explainability & Interpretability in NLP

Challenge:	a new framework for complex reasoning with LLMs is developed to improve reasoning proof accuracy and interpretability.
Approach:	They propose to use LLMs to generate search logs that can be interpreted into human-readable reasoning proofs.
Outcome:	The proposed framework improves reasoning accuracy but lacks interpretability due to black-box nature of the solvers.

SLoW: Select Low-frequency Words! Automatic Dictionary Selection for Translation on Large Language Models (2025.emnlp-main)

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Hongyuan Lu, Zixuan Li, Zefan Zhang, Wai Lam

Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning (2024.findings-emnlp)

Challenge:	Existing large language models only support hundreds of languages, and they are usually limited in English.
Approach:	They propose a task to automatically select which dictionary to use to enhance translation . they call it Select Low-frequency Words!, which inherits advantage of dictionary-based methods .
Outcome:	The proposed method can save tokens and improve translation performance on 100 languages.

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Sen Yang, Leyang Cui, Deng Cai, Xinting Huang, Shuming Shi, Wai Lam

JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning (2025.findings-acl)

Challenge:	Large language models (LLMs) have shown remarkable capabilities to understand and generate human languages, supporting applications such as question answering, coding, and psychological counseling.
Approach:	They propose strategies to save annotation budgets while achieving competitive or even better performances for iterative preference learning.
Outcome:	The proposed methods save annotation budgets while achieving better performance.

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Chang Gao, Wenxuan Zhang, Guizhen Chen, Wai Lam

Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences (2023.findings-acl)

Challenge:	Existing text-to-text methods struggle with issues such as generalization, robustness, and controllability due to their lack of explicit task structures.
Approach:	They propose a structure-to-structure approach that uses JSON structures to represent tasks.
Outcome:	The proposed method outperforms TextTuning in terms of performance, robustness, and controllability across different scenarios.

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Chaojun Wang, Yang Liu, Wai Lam

Robustness in NLP

Challenge:	Existing studies show that intermediate supervision signals benefit various tasks such as math word problems and commonsense reasoning.
Approach:	They propose to introduce an inductive bias that reflects a domain-agnostic principle of translation and a full-permutation multi-task learning to alleviate spurious correlations.
Outcome:	The proposed signals reduce spurious correlations and spurious hallucinations on out-of-domain translation, and are especially promising in low-resource scenarios.

CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite (2024.lrec-main)

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Yifei Yuan, Chen Shi, Wang Runze, Liyi Chen, Renjun Hu, Zengming Zhang, Feijun Jiang, Wai Lam

QuaSE: Sequence Editing under Quantifiable Guidance (D18-1)

Challenge:	Recent advances in conversational IR systems have seen a resurgent interest in conversation . generative query rewrite generates reconstructed query based on the conversation history .
Approach:	They propose to use unlabeled data to make further improvements using contrastive co-training paradigm.
Outcome:	The proposed model is robust to noise and language style shift under few-shot and zero-shot scenarios.

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Yi Liao, Lidong Bing, Piji Li, Shuming Shi, Wai Lam, Tong Zhang

Challenge:	Existing methods for Quantifiable Sequence Editing (QuaSE) require editing an input sequence to generate an output that satisfies a numerical outcome value measuring a certain property of the sequence.
Approach:	They propose a framework for Quantifiable Sequence Editing that allows editing an input sequence to change an outcome and keep the content.
Outcome:	The proposed framework disentangles outcome factor and content factor from the input sentence to allow editing to change the outcome and keep the content.

Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)

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Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations (2022.acl-long)

Challenge:	Existing studies focus on predicting the four elements in one shot, instead of predicting them all.
Approach:	They propose a task to jointly detect all sentiment elements in quads for a given opinionated sentence.
Outcome:	The proposed method can generate the semantics of the sentiment elements in the natural language form.

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Chun Hei Lo, Wai Lam, Hong Cheng