Papers by Lidong Bing

SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)

Challenge:	Existing research on multimodal relation extraction (MRE) faces internal-information over-utilization and external-information under-exploitation.
Approach:	They propose a framework that implements internal-information screening and external-information exploiting to address these challenges.
Outcome:	The proposed framework outperforms the current best model on the benchmark dataset.

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Yue Deng, Wenxuan Zhang, Sinno Pan, Lidong Bing

Interventional Training for Out-Of-Distribution Natural Language Understanding (2022.emnlp-main)

Challenge:	Sentiment analysis models often fail to capture the broader complexities of sentiment analysis.
Approach:	They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews .
Outcome:	The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% .

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Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing

Challenge:	Existing methods for NLU training use only known and single confounders, but in many NLU tasks the confounder can be unknown and multifactorial.
Approach:	They propose a method that performs multi-granular intervention with identified multifactorial confounders by using a bottom-up automatic intervention method.
Outcome:	The proposed method performs multi-granular intervention with identified multifactorial confounders on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification.

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation (2021.acl-long)

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Ruidan He, Linlin Liu, Hai Ye, Qingyu Tan, Bosheng Ding, Liying Cheng, Jiawei Low, Lidong Bing, Luo Si

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation (2020.emnlp-main)

Challenge:	Existing studies have shown that adapter-based tuning is more parameter-efficient than fine-tuning.
Approach:	They propose to add adapter modules to a pretrained language model and update the parameters of adapter module when learning on a downstream task.
Outcome:	The proposed method outperforms fine-tuning on low-resource and cross-lingual tasks and settings.

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Yan Zhang, Zhijiang Guo, Zhiyang Teng, Wei Lu, Shay B. Cohen, Zuozhu Liu, Lidong Bing

Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding (2023.emnlp-demo)

Challenge:	AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMRs) Graph Convolution Networks (GCNs) are not able to capture non-local information and follow a local (first-order) information aggregation scheme.
Approach:	They propose a dynamic fusion mechanism that captures richer non-local interactions . they propose weight tied convolutions and group graph convolution to reduce memory usage .
Outcome:	The proposed model outperforms state-of-the-art models on two benchmark datasets with significantly fewer parameters while maintaining the model capacity.

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Hang Zhang, Xin Li, Lidong Bing

Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)

Challenge:	Large Language Models (LLMs) are capable of understanding multi-modal content, but textonly human-computer interaction is not sufficient for many application scenarios.
Approach:	They propose a video-to-text generation task and a multi-modal framework that bootstraps cross-modal training from frozen pre-trained visual & audio encoders and frozen LLMs.
Outcome:	The proposed framework can understand both visual and auditory content in video and generate meaningful responses grounded in the visual and audio information presented in the videos.

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Guizhen Chen, Liying Cheng, Anh Tuan Luu, Lidong Bing

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

Challenge:	Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence.
Approach:	They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets .
Outcome:	The proposed model performs well on argument mining and argument generation tasks.

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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.

Improving Low-Resource Named Entity Recognition using Joint Sentence and Token Labeling (2020.acl-main)

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Canasai Kruengkrai, Thien Hai Nguyen, Sharifah Mahani Aljunied, Lidong Bing

AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach (2023.findings-acl)

Challenge:	Existing models for named entity recognition (NER) use sentence-level labels, which are expensive to obtain, to improve NER.
Approach:	They propose a sentence-level named entity recognition model that uses sentence-based labels that are easy to obtain.
Outcome:	The proposed model produces 3.78%, 4.20%, 2.08% improvements in F1 over the baseline on e-commerce product titles in Vietnamese, Thai, and Indonesian, respectively.

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Jia Guo, Liying Cheng, Wenxuan Zhang, Stanley Kok, Xin Li, Lidong Bing

Syntactic Text Processing

Challenge:	Argument mining involves multiple subtasks, but each one is insufficient for understanding argumentative structure and reasoning process.
Approach:	They propose a quadruplet extraction task that extracts four argumentative components . they use a generative quadragging module to augment the training of the generative framework .
Outcome:	The proposed method can extract arguments from a large-scale dataset.

Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework (2023.acl-long)

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Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, Lidong Bing

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have a number of shortcomings, including lack of factual correctness.
Approach:	They propose a framework to increase prediction factuality by post-editing reasoning chains . they propose to use large language models to generate interpretable reasoning chains.
Outcome:	The proposed framework leads to accuracy improvements in open-domain question-answering tasks.

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction (2023.findings-acl)

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Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy Nguyen, Zhen Hai, Lidong Bing, Anh Tuan Luu

Domain Generalization for Text Classification with Memory-Based Supervised Contrastive Learning (2022.coling-1)

Challenge:	Existing studies have shown that FCNNs perform inefficient splitting for review features, making it difficult to clearly differentiate helpful from unhelpful reviews.
Approach:	They propose a listwise attention network that captures the MRHP ranking context and a pairwise optimization objective that enhances model generalization.
Outcome:	The proposed framework achieves state-of-the-art results and polished generalization performance on two large-scale MRHP benchmark datasets.

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Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng

Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)

Challenge:	Existing approaches to cross-domain text classification focus on one-to-one domain adaptation.
Approach:	They propose a framework for domain generalization that uses contrastive learning with a memory-saving queue.
Outcome:	The proposed framework outperforms state-of-the-art methods on Amazon review sentiment datasets and rumour detection datasets.

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, Sharifah Mahani Aljunied

Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (D19-1)

Challenge:	Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED.
Approach:	They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED.
Outcome:	The proposed dataset improves on the existing DocRED dataset by 13 F1 points.

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Ran Le, Wenpeng Hu, Mingyue Shang, Zhenjun You, Lidong Bing, Dongyan Zhao, Rui Yan

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

Challenge:	In multi-party conversations, addressee information is not always explicit . researchers have spent great efforts to understand conversations between two participants, which is known as multi-part conversation.
Approach:	They propose a who-to-whom model which models users and utterances in a conversation session jointly in an interactive way.
Outcome:	The proposed model outperforms baseline models on the Ubuntu Multi-Party Conversation Corpus and shows consistent improvements.

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Zihao Fu, Bei Shi, Lidong Bing, Wai Lam

PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns (2024.findings-acl)

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.

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Yew Ken Chia, Vernon Toh, Deepanway Ghosal, Lidong Bing, Soujanya Poria

MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)

Challenge:	recognizing patterns and abstracting concepts are key to general intelligence, we show . state-of-the-art large multimodal models struggle to generalize well to simple abstract patterns .
Approach:	They evaluate large multimodal models with abstract patterns based on colors, numbers, sizes, and shapes.
Outcome:	The proposed model fails to generalize well to simple abstract patterns, the study shows . the model fails on single-concept puzzles, despite its sophistication .

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Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, Luo Si

Argument Pair Extraction via Attention-guided Multi-Layer Multi-Cross Encoding (2021.acl-long)

Challenge:	a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge.
Approach:	They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal .
Outcome:	The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences .

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Liying Cheng, Tianyu Wu, Lidong Bing, Luo Si

Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation (2022.findings-acl)

Challenge:	Argument pair extraction (APE) is a research task for extracting arguments from two passages and identifying potential argument pairs.
Approach:	They propose a novel attention-guided multi-layer multi-cross encoding scheme that processes two passages with two individual sequence encoders and updates their representations using each other’s attention.
Outcome:	The proposed model significantly improves the performance over several alternatives.

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Qingyu Tan, Ruidan He, Lidong Bing, Hwee Tou Ng

Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023.findings-emnlp)

Challenge:	Document-level relation extraction (DocRE) is a more challenging task than sentence-level one.
Approach:	They propose a semi-supervised framework for document-level relation extraction with three components . they use an axial attention module for learning the interdependency among entity-pairs .
Outcome:	The proposed model outperforms baseline models on two DocRE datasets and outperformed previous models on human annotated data and distantly supervised data.

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Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You, Lidong Bing

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

Challenge:	ChatGPT and GPT-4 are popular as evaluation metric for complex generative tasks . however, they are not ready as human replacements due to significant limitations .
Approach:	They conduct extensive analysis to examine the stability and reliability of LLMs as automatic evaluators for abstractive summarization.
Outcome:	The proposed methods outperform the commonly used automatic metrics but are not ready for human evaluation due to significant limitations.

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

Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Prediction (2022.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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Thong Nguyen, Xiaobao Wu, Anh Tuan Luu, Zhen Hai, Lidong Bing

MulDA: A Multilingual Data Augmentation Framework for Low-Resource Cross-Lingual NER (2021.acl-long)

Challenge:	Modern review helpfulness prediction systems focus on polishing cross-modal representations and suffer from inferior optimization.
Approach:	They propose a method to polish cross-modal relation representations by learning mutual information through contrastive learning.
Outcome:	The proposed framework outperforms baselines and achieves state-of-the-art results on two publicly available datasets.

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Linlin Liu, Bosheng Ding, Lidong Bing, Shafiq Joty, Luo Si, Chunyan Miao

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

Challenge:	Existing approaches to cross-lingual NER are labeled sequence translation and instance-based transfer via machine translation (MT) Existing methods to cross NER include label projection and labeling, but they are expensive and time-consuming.
Approach:	They propose a simple but effective labeled sequence translation method to translate source-language training data to target languages and avoids word order change and entity span determination.
Outcome:	The proposed method avoids word order change and entity span determination and can be generalized with the language-specific features from the target-language synthetic data and the language independent features from multilingual synthetic data.

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Sen Yang, Xin Li, Lidong Bing, Wai Lam

ParaICL: Towards Parallel In-Context Learning (2025.naacl-long)

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 .

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Xingxuan Li, Xuan-Phi Nguyen, Shafiq Joty, Lidong Bing

Estimating Marginal Probabilities of n-grams for Recurrent Neural Language Models (D18-1)

Challenge:	Existing methods to improve ICL performance are limited by the length of the input context.
Approach:	They propose a method that utilizes all demonstration examples without exceeding the manageable context length.
Outcome:	The proposed method can be scaled up to integrate with existing methods.

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Thanapon Noraset, Doug Downey, Lidong Bing

Is GPT-3 a Good Data Annotator? (2023.acl-long)

Challenge:	Recurrent neural network language models (RNNs) only estimate probabilities for complete sequences of text, whereas some applications require context-independent phrase probabilities instead.
Approach:	They propose a method to alter the RNNLM training to make it more accurate at marginal estimation.
Outcome:	The proposed method is effective compared to baselines including the traditional RNNLM probability and importance sampling approach.

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Bosheng Ding, Chengwei Qin, Linlin Liu, Yew Ken Chia, Boyang Li, Shafiq Joty, Lidong Bing

Is GPT-4 a Good Data Analyst? (2023.findings-emnlp)

Challenge:	Data annotation is the process of labeling data that could be used to train machine learning models.
Approach:	They evaluate the performance of a large-scale language model developed by OpenAI . they compare it with traditional methods and analyze its output on a range of tasks .
Outcome:	The proposed model has shown impressive performance on a range of NLP tasks.

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Liying Cheng, Xingxuan Li, Lidong Bing

Evaluating Psychological Safety of Large Language Models (2024.emnlp-main)

Challenge:	Large language models (LLMs) have shown their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc.
Approach:	They propose to use GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains.
Outcome:	The proposed framework compares GPT-4 with human data analysts to perform end-to-end data analysis with databases from a wide range of domains.

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Xingxuan Li, Yutong Li, Lin Qiu, Shafiq Joty, Lidong Bing

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	a recent study evaluated the psychological safety of large language models.
Approach:	They designed unbiased prompts to evaluate the psychological safety of large language models.
Outcome:	The proposed prompts showed that they were fine-tuned with behavioral metrics to reduce toxicity.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Enhancing Cross-lingual Prompting with Dual Prompt Augmentation (2023.findings-acl)

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

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Meng Zhou, Xin Li, Yue Jiang, Lidong Bing

RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (2022.findings-acl)

Challenge:	a recent study shows that prompting is superior for multilingual/cross-lingual problems . despite its effectiveness on English tasks, its potential for cross-lingual problem is under-explored .
Approach:	They propose a framework for prompting that can be used to augment cross-lingual prompts.
Outcome:	The proposed framework achieves 46.54% with only 16 English training examples per class, significantly better than fine-tuning.

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Yew Ken Chia, Lidong Bing, Soujanya Poria, Luo Si

Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions (2025.acl-long)

Challenge:	Existing approaches to extract relation triplets require large datasets and a fixed set of relations.
Approach:	They propose to use a sentence-based task setting to generalize relation extraction methods to unseen relation sets.
Outcome:	The proposed method can extract multiple relation triplets in a sentence using language model prompts and structured text approaches.

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Ruochen Zhao, Wenxuan Zhang, Yew Ken Chia, Weiwen Xu, Deli Zhao, Lidong Bing

A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) are evolving rapidly and require manual evaluations.
Approach:	They propose an LLM-powered framework that automates the entire evaluation process using LLM agents.
Outcome:	The proposed framework shows a 92.14% correlation with human preferences, surpassing all previous expert-annotated benchmarks without any manual efforts.

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Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, Soujanya Poria

Knowledge Representation

Challenge:	Existing methods do not consider qualifier attributes for each relation triplet, such as time, quantity or location.
Approach:	They propose a hyper-relational extraction task to extract more specific facts from text using qualifiers.
Outcome:	The proposed model outperforms baselines and reveal possible directions for future research.

Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024.acl-long)

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Xuan-Phi Nguyen, Mahani Aljunied, Shafiq Joty, Lidong Bing

LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models (2023.emnlp-main)

Challenge:	Large language models (LLMs) are known to perform tasks by simply observing few exemplars, but performance among under-represented languages falls behind due to pre-training data imbalance.
Approach:	They propose to assemble synthetic exemplars from high-resource languages to prompt LLMs to translate from any language into English and use them to create intra-lingual exemplar models to perform tasks in target languages.
Outcome:	The proposed method outperforms supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.

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Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Lee

Analyzing LLMs’ Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations (2025.acl-long)

Challenge:	Large language models (LLMs) have shown unprecedented performance across various tasks.
Approach:	They propose an easy-to-use framework that integrates adapters into LLMs . they evaluate adapters on 14 datasets from two different reasoning tasks .
Outcome:	The proposed framework can be used to fine-tune open-access language models with task-specific data and instruction data.

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Chenghao Xiao, Hou Pong Chan, Hao Zhang, Mahani Aljunied, Lidong Bing, Noura Al Moubayed, Yu Rong

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

Challenge:	Understanding the knowledge boundaries of Large Language Models (LLMs) is crucial to prevent hallucination, but research on the knowledge boundary perceptions of LLMs has predominantly focused on English.
Approach:	They propose a training-free alignment method that effectively transfers knowledge boundary perception ability across languages, thereby helping reduce hallucination risk in low-resource languages.
Outcome:	The proposed method reduces hallucination risk in low-resource languages by fine-tuning on bilingual question pair translation.

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

Hierarchical Pointer Net Parsing (D19-1)

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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Linlin Liu, Xiang Lin, Shafiq Joty, Simeng Han, Lidong Bing

Challenge:	Existing approaches to parsing are greedy transition-based and globally optimized . however, the decision-making process is based on local information, causing error propagation to subsequent steps.
Approach:	They propose hierarchical pointer network parsers and apply them to dependency and sentence-level discourse parsing tasks.
Outcome:	The proposed method outperforms existing methods and sets new state-of-the-art methods on benchmark datasets.

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

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Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.findings-acl)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng

Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning (D19-1)

Challenge:	Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage.
Approach:	They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores.
Outcome:	The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated.

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Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang

Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)

Challenge:	Existing studies fail to provide comprehensive service satisfaction analysis . Existing models fail to include satisfaction polarity classification and sentimental utterance identification .
Approach:	They propose a model that predicts customer sentiments and aggregates them into service satisfaction polarity.
Outcome:	The proposed model predicts customer sentiments and aggregates them into service satisfaction polarity and reasoning clues.

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Lu Xu, Hao Li, Wei Lu, Lidong Bing

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

Challenge:	Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
Approach:	They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach.
Outcome:	The proposed model improves performance on multiple datasets and compares with existing models.

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Chang Gao, Wenxuan Zhang, Wai Lam, Lidong Bing

AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging (2025.naacl-long)

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.

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Yiran Zhao, Wenxuan Zhang, Huiming Wang, Kenji Kawaguchi, Lidong Bing

M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework (2025.emnlp-main)

Challenge:	Large Language Models excel in highresource languages but underperform in lowresource ones.
Approach:	They propose a cross-lingual transfer method that decouples "task ability" from "language ability" they propose to use adaptive adapter merging to obtain target adapters by combining other adapters.
Outcome:	The proposed method outperforms existing methods in highresource languages . it decouples "task ability" from "language ability" but fails to fully separate "task capability" from the "source language"

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Yew Ken Chia, Liying Cheng, Hou Pong Chan, Maojia Song, Chaoqun Liu, Mahani Aljunied, Soujanya Poria, Lidong Bing

Information Retrieval

Challenge:	Existing benchmarks for large multimodal models focus on short documents with less than 50 pages and are limited to extraction-based questions.
Approach:	They propose a retrieval-aware tuning approach to improve the accuracy of multimodal document reading by 4.6%.
Outcome:	The proposed framework improves the accuracy of model responses by 4.6% compared to existing benchmarks on documents with hundreds of pages and longer documents with more complex content.

Bootstrapped Unsupervised Sentence Representation Learning (2021.acl-long)

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Yan Zhang, Ruidan He, Zuozhu Liu, Lidong Bing, Haizhou Li

Representation Learning

Challenge:	Existing approaches to learn sentence representations rely on quality labeled data.
Approach:	They propose a Siamese Network which maximizes similarity between two augmented views of each sentence.
Outcome:	The proposed method outperforms state-of-the-art methods on STS and classification tasks.

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

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models (2024.findings-emnlp)

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 .

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Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See-Kiong Ng, Lidong Bing, Roy Lee

An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction (N19-1)

Challenge:	Existing image instruction fine-tuning datasets do not fully exploit visual information to enhance multimodal reasoning capabilities of Large language models (LLMs).
Approach:	They propose a LLaVA-based model fine-tuned with MathV360K to bridge this gap by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs.
Outcome:	The proposed model improves the multimodal reasoning capabilities of LLaVA-1.5 and demonstrates enhanced generalizability on the MMMU benchmark.

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Wang Chen, Hou Pong Chan, Piji Li, Lidong Bing, Irwin King

Information Retrieval

Challenge:	Existing methods on keyphrase generation are purely extractive or generative . however, extractive methods cannot predict absent keyphrases which are not in the document.
Approach:	They propose a multi-task learning framework that jointly learns an extractive model and a generative model.
Outcome:	The proposed approach outperforms the state-of-the-art methods on five keyphrase generation tasks.

EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning (2026.acl-long)

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Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, Yafeng Deng

Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)

Challenge:	Existing memory systems for LLMs store isolated records and retrieve fragments . Existing systems store isolated data and fragments, limiting their ability to consolidate evolving experience and resolve conflicts.
Approach:	They propose an engram-inspired memory operating system that implements an 'engram'-inspired lifecycle for computational memory.
Outcome:	Experiments on LoCoMo, LongMemEval, and PersonaMeM-v2 show that EverMemeOS outperforms state-of-the-art methods on memory-augmented reasoning tasks.

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Huiming Wang, Liying Cheng, Wenxuan Zhang, De Wen Soh, Lidong Bing

Reasoning Implicit Sentiment with Chain-of-Thought Prompting (2023.acl-short)

Challenge:	Existing DA methods for named entity recognition (NER) are costly and labor-intensive to acquire, necessitating innovative approaches to data scarcity.
Approach:	They propose an order-agnostic data augmentation solution that exploits the order-based property in the training phase of sequence-to-sequence NER methods for data augmented.
Outcome:	The proposed method significantly enhances the few-shot capabilities of pre-trained language models in low-resource settings.

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Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, Tat-Seng Chua

Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations (2023.acl-long)

Challenge:	In implicit sentiment analysis, the opinion cues come in an implicit and obscure manner.
Approach:	They propose a three-step prompting principle for THOR to step-by-step induce the implicit aspect, opinion and finally the sentiment polarity.
Outcome:	The proposed framework pushes the state-of-the-art (SoTA) by over 6% F1 on supervised setup and more strikingly, boosts the SoTA by over 50% F1 with THOR+GPT3.

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Linlin Liu, Xingxuan Li, Megh Thakkar, Xin Li, Shafiq Joty, Luo Si, Lidong Bing

Responsible & Trustworthy NLP

Challenge:	Using hidden representations, pretrained language models are prone to overfitting due to the huge amount of parameters.
Approach:	They propose a method that inserts random autoencoders between hidden layers of a PLM to transform activations from the previous layers into multi-view compressed representations before feeding them into the upper layers.
Outcome:	The proposed method improves performance across sequence- and token-level lowresource tasks.

Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings (2022.coling-1)

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Linlin Liu, Thien Hai Nguyen, Shafiq Joty, Lidong Bing, Luo Si

Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models (2023.acl-long)

Challenge:	Existing approaches to learn cross-lingual word embeddings are sense agnostic . a novel framework to align contextual embeddables at the sense level is proposed .
Approach:	They propose a framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only.
Outcome:	The proposed framework improves word sense disambiguation tasks by leveraging bilingual dictionaries . compared with baseline results, the proposed models achieve 0.52%, 2.09% and 1.29% performance improvements .

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Qingyu Tan, Hwee Tou Ng, Lidong Bing

DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks (2020.emnlp-main)

Challenge:	Recent time-dependent question answering datasets tend to be biased in either their coverage of time spans or question types.
Approach:	They propose a temporal reasoning framework based on temporal span extraction and time-sensitive reinforcement learning to improve the temporal ability of large language models.
Outcome:	The proposed framework improves the temporal reasoning capability of large language models by using temporal span extraction and time-sensitive reinforcement learning.

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Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, Chunyan Miao

Syntactic Text Processing

Challenge:	Data augmentation techniques are widely used to improve machine learning performance . however, due to the complexity of language, it is difficult to generalize such rules for languages.
Approach:	They propose a method to generate high quality synthetic data for low-resource tagging tasks . they use unlabeled data only and unlabelled data plus a knowledge base .
Outcome:	The proposed method outperforms baselines on NER, part of speech and target based sentiment analysis tasks.

SentBS: Sentence-level Beam Search for Controllable Summarization (2022.emnlp-main)

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Chenhui Shen, Liying Cheng, Lidong Bing, Yang You, Luo Si

Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction (2023.eacl-main)

Challenge:	Structure-controlled summarization is a useful and interesting research direction . current structure-controlling methods have limited effectiveness in enforcing the desired structure.
Approach:	They propose a sentence-level beam search generation method to select suitable sentences for subsequent generations.
Outcome:	The proposed method significantly reduces structural discrepancies by 68% on a structure-controlled dataset.

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Jia Guo, Stanley Kok, Lidong Bing

Sampling Better Negatives for Distantly Supervised Named Entity Recognition (2023.findings-acl)

Challenge:	Document-level relation extraction (DocRE) predicts relations for entity pairs relying on context-dependent reasoning . a large number of annotation errors can make it difficult to distinguish large semantically close relations .
Approach:	They propose a loss function to improve discriminability and robustness for DocRE . they also propose supervised contrastive learning and negative label sampling strategy .
Outcome:	The proposed method achieves state-of-the-art results on the DocRED dataset and its recently cleaned version.

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Lu Xu, Lidong Bing, Wei Lu

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)

Challenge:	Existing supervised named entity recognition approaches rely on human annotations.
Approach:	They propose a method to select negative samples with high similarities with positive samples . they propose to use automatically labeled training data instead of human annotations .
Outcome:	The proposed method achieves consistent performance improvements on four distantly supervised NER datasets.

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Liying Cheng, Lidong Bing, Ruidan He, Qian Yu, Yan Zhang, Luo Si

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)

Challenge:	Argument mining (AM) is a computational process that is used to analyze information in a debating system.
Approach:	They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks .
Outcome:	The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks .

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Chunyan Miao

Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates (D18-1)

Challenge:	Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance.
Approach:	They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER)
Outcome:	The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs.

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Di Chen, Jiachen Du, Lidong Bing, Ruifeng Xu

SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia (2025.findings-naacl)

Challenge:	Existing models for agreement/disagreement in debates lack the ability to model these two factors together.
Approach:	They propose a hybrid attention model which combines self and cross attention mechanism to locate salient part from textual context and interaction between users.
Outcome:	The proposed model outperforms the state-of-the-art models on three (dis)agreement inference datasets.

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Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing

Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)

Challenge:	Large Language Models (LLMs) have shown remarkable performance across various English benchmarks, including both human exam datasets such as MMLU and instruction-following datasets.
Approach:	They introduce two new benchmarks to evaluate the capabilities of Large Language Models in Southeast Asian (SEA) application scenarios.
Outcome:	The proposed benchmarks show that they can discern LLM performance on SEA language tasks compared to their translated benchmarks.

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Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing

AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation (2024.emnlp-main)

Challenge:	Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data.
Approach:	They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data.
Outcome:	The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design.

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Ziyang Luo, Xin Li, Hongzhan Lin, Jing Ma, Lidong Bing

Better Feature Integration for Named Entity Recognition (2021.naacl-main)

Challenge:	proprietary large language models (LLMs) have demonstrated impressive code generation performance.
Approach:	They propose an adaptive module-based model that refines the direct response distillation process by modular decomposition and adaptive response evolution.
Outcome:	The proposed framework outperforms baseline model and code generation methods on three popular benchmarks.

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Lu Xu, Zhanming Jie, Wei Lu, Lidong Bing

Syntactic Text Processing

Challenge:	Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant.
Approach:	They propose a model that incorporates both types of features with a Synergized-LSTM which captures how the two types of feature interact.
Outcome:	The proposed model achieves better performance than previous approaches while requiring fewer parameters.

Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)

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Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing

An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020.emnlp-main)

Challenge:	Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level.
Approach:	They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions .
Outcome:	The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features.

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Yan Zhang, Ruidan He, Zuozhu Liu, Kwan Hui Lim, Lidong Bing

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

Challenge:	Sentence BERT is inefficient for sentence-pair tasks as it needs to evaluate combinatorially many sentence pairs which is very time-consuming.
Approach:	They propose a lightweight extension on top of BERT and a self-supervised learning objective to derive meaningful sentence embeddings in an unsupervised manner.
Outcome:	The proposed method outperforms baselines on common semantic textual similarity tasks and downstream supervised tasks and achieves performance competitive with supervised methods on various tasks.

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

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

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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Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam

Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)

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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Chaoqun Liu, Wenxuan Zhang, Yiran Zhao, Anh Tuan Luu, Lidong Bing

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews (2021.acl-long)

Challenge:	Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora.
Approach:	They extend the evaluation to real-world user queries and non-English-centric LLMs . they show that translation into English can boost LLM performance on NLP tasks .
Outcome:	The proposed evaluation extends to user queries and non-English-centric LLMs . it shows that translation into English can boost performance on NLP tasks, but not universally optimal .

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Junhao Liu, Zhen Hai, Min Yang, Lidong Bing

FineReason: Evaluating and Improving LLMs’ Deliberate Reasoning through Reflective Puzzle Solving (2025.acl-long)

Challenge:	Existing review helpfulness prediction tasks rely on text and image modalities to analyze review helpfuliness.
Approach:	They propose a task to analyze review helpfulness from text and visual modalities and propose 'multi-perspective coherent reasoning' method to combine coherence between product and review is proposed.
Outcome:	The proposed method can lead to performance increase of 8.5% compared to the best performing text-only model.

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Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong

SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning (2022.coling-1)

Challenge:	Recent advances in large language models (LLMs) highlight an important shift from the “System 1” way of quick reactions to the “system 2” style of reflection-and-correction problem solving.
Approach:	They propose a logic-puzzle benchmark for systematic evaluation of large language models' reasoning capabilities that decomposes each puzzle into atomic steps.
Outcome:	The proposed model improves on state checking and state transition tasks and demonstrates gains in reasoning by up to 5.1%.

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Wei Han, Hui Chen, Zhen Hai, Soujanya Poria, Lidong Bing

Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths (2024.findings-emnlp)

Challenge:	e-commerce has become a research hotspot for review helpfulness prediction . a new approach to help predict helpfulness of multimodal product reviews is proposed .
Approach:	They propose a machine learning task to identify helpfulness of multimodal product reviews . they use a probe-based strategy to enforce high attention weights on regions of greater significance .
Outcome:	The proposed model achieves state-of-the-art performance with lower memory consumption on two benchmark datasets with three categories.

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Yew Ken Chia, Guizhen Chen, Weiwen Xu, Anh Tuan Luu, Soujanya Poria, Lidong Bing

Towards Robust Temporal Reasoning of Large Language Models via a Multi-Hop QA Dataset and Pseudo-Instruction Tuning (2024.findings-acl)

Challenge:	Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities, but they may still falter on more complex problems, making errors that disrupt their reasoning paths.
Approach:	They propose a framework that encourages favorable branches at each reasoning step while penalizing unfavorable ones, enhancing the model’s overall problem-solving performance.
Outcome:	The proposed framework improves reasoning performance on multi-step reasoning tasks such as math word problems and science-based exam questions.

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Qingyu Tan, Hwee Tou Ng, Lidong Bing

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

Challenge:	Existing LLMs lack the ability to deal with temporal knowledge.
Approach:	They propose a temporal question-answering dataset Complex-TR that focuses on multi-answered and multi-hop temporal reasoning and propose augmentation strategy to improve LLMs' performance.
Outcome:	The proposed dataset improves LLMs’ performance on temporal QA benchmarks by significant margins.

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Weiwen Xu, Xin Li, Wai Lam, Lidong Bing

Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels (2025.coling-main)

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.

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Chaoqun Liu, Qin Chao, Wenxuan Zhang, Xiaobao Wu, Boyang Li, Anh Tuan Luu, Lidong Bing

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

Challenge:	Pre-trained language models have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels.
Approach:	They propose a new paradigm termed zero-to-strong generalization that prompts LLMs to annotate unlabeled data and retain high-quality labels by filtering.
Outcome:	The proposed framework outperforms pre-trained language models on extensive classification and reasoning tasks on multiple model sizes.

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

Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization (2025.acl-long)

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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Yao Xiao, Hai Ye, Linyao Chen, Hwee Tou Ng, Lidong Bing, Xiaoli Li, Roy Ka-Wei Lee

A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis (D19-1)

Challenge:	Large language models generate unintended outputs due to their unsupervised nature.
Approach:	They propose a method to construct preference pairs of selected and rejected LLMs by repeated random sampling to improve alignment performance.
Outcome:	The proposed method improves performance as the sample size increases.

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Chuang Fan, Hongyu Yan, Jiachen Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, Ruibin Mao

Sentiment Analysis in the Era of Large Language Models: A Reality Check (2024.findings-naacl)

Challenge:	Emotion cause analysis aims to identify the reasons behind emotions . previous models focus on learning architecture with local textual information .
Approach:	They propose a method to extract emotion cause with hierarchical neural model and knowledge-based regularizations by sentiment lexicon and common knowledge.
Outcome:	The proposed method outperforms baselines on two public datasets in different languages and outperformed competitive baselines by 2.08%.

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Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Pan, Lidong Bing

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

Challenge:	Sentiment analysis (SA) has been a long-standing research area in natural language processing.
Approach:	They propose a benchmark to evaluate LLMs' SA abilities and propose 'sentiEval' benchmark to be used for a more comprehensive evaluation.
Outcome:	The proposed benchmark outperforms small language models on 26 datasets on 13 tasks and compared them with LLMs trained on domain-specific datasets.

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

SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)

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.

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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing

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

Challenge:	Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages.
Approach:	They propose a series of language models that specifically focuses on Southeast Asian languages.
Outcome:	SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations .

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

Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning (D19-1)

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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Zheng Li, Xin Li, Ying Wei, Lidong Bing, Yu Zhang, Qiang Yang

Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty (2026.findings-acl)

Challenge:	Existing methods to extract aspects and sentiments are limited due to lack of annotated sequence data.
Approach:	They propose a Selective Adversarial Learning method to align latent correlation vectors . they propose tagging a set of aspect boundary tags and sentiment tags to create a joint label space .
Outcome:	The proposed method can learn weights for words to achieve fine-grained adaptation.

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Yao Xiao, Jung-jae Kim, Roy Ka-Wei Lee, Lidong Bing

Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)

Challenge:	incorporating difficult prompts into training fails to enhance overall performance, e.g., as prompt difficulty decreases.
Approach:	They investigate how prompts of varying difficulty influence self-play preference optimization . they use the reward of sampled responses of a prompt as a proxy for its difficulty .
Outcome:	The proposed model improves on difficult prompts and easy prompts, but fails to train on difficult ones and learns from failures.

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Mingyue Shang, Piji Li, Zhenxin Fu, Lidong Bing, Dongyan Zhao, Shuming Shi, Rui Yan

Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction (2021.acl-long)

Challenge:	Text style transfer task has long suffered from the shortage of parallel data .
Approach:	They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it.
Outcome:	The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning.

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Lu Xu, Yew Ken Chia, Lidong Bing

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

Challenge:	Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each word and opinion word.
Approach:	They propose a span-level approach which explicitly considers the interaction between whole spans of targets and opinions when predicting their sentiment relation.
Outcome:	The proposed approach improves on triplets with multi-word targets and opinions . it explicitly considers the interaction between whole spans of targets and opinion words .

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

Responsible & Trustworthy NLP

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.

Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks (2025.acl-long)

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Xingxuan Li, Weiwen Xu, Ruochen Zhao, Fangkai Jiao, Shafiq Joty, Lidong Bing

Programming Languages in NLP

Challenge:	Existing approaches to problem-solving for large language models fail to provide accurate reasoning and factual accuracy.
Approach:	They propose a framework that leverages fine-tuned critic models to guide reasoning and retrieval processes.
Outcome:	The proposed framework outperforms baselines on domain-knowledge-intensive tasks . it can be used to iterate retrieval and reasoning, and improve retrieval relevance .

Variational Autoregressive Decoder for Neural Response Generation (D18-1)

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Jiachen Du, Wenjie Li, Yulan He, Ruifeng Xu, Lidong Bing, Xuan Wang

Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020.emnlp-main)

Challenge:	Existing variational Bayesian models generate responses from a single latent variable, which is not sufficient to model high variability in responses.
Approach:	They propose a conditional variable auto-encoder that sequentially introduces latent variables to condition the generation of each word in the response sequence.
Outcome:	Empirical results show that the proposed model improves on state-of-the-art models on Opensubtitle and Reddit datasets.

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Lu Xu, Lidong Bing, Wei Lu, Fei Huang

Pruning General Large Language Models into Customized Expert Models (2025.findings-acl)

Challenge:	Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span.
Approach:	They propose a model that extracts aspect-specific opinion spans and evaluates sentiment polarity by exploiting extracted opinion features.
Outcome:	The proposed model extracts aspect-specific opinion spans and evaluates sentiment polarity using extracted opinion features.

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Yiran Zhao, Guizhen Chen, Kenji Kawaguchi, Lidong Bing, Wenxuan Zhang

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

Challenge:	Large language models (LLMs) require significant computational resources to maintain their general capabilities.
Approach:	They propose a Custom Pruning method to prune a large general model into a smaller lightweight expert model, positioned along the "language", "domain" and "task" dimensions.
Outcome:	The proposed method outperforms existing pruning methods and achieves minimal loss in both expert and general capabilities across models from different model families and sizes.

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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.

Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training (2020.emnlp-main)

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Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing

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

Challenge:	Adapting pre-trained language models (PrLMs) to new domains has gained much attention . Adaptation of PrLMs to newdomains is important, but requires fine-tuning .
Approach:	They propose to use PrLMs to adapt to new domains without fine-tuning . they use class-aware feature self-distillation to learn discriminative features .
Outcome:	The proposed model can learn discriminative features from pre-trained language models without fine-tuning.

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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.

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.

A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization (2023.findings-emnlp)

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Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You, Lidong Bing

Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples (2022.emnlp-main)

Challenge:	Pre-trained language models have been used for abstractive single-document summarization (SDS) but they may not be suitable for multi-document summary (MDS)
Approach:	They propose to enforce hierarchy on both encoder and decoder to facilitate multi-document interactions for MDS.
Outcome:	Xiao et al. (2019) outperforms or is competitive with the previous best models.

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Linlin Liu, Xin Li, Ruidan He, Lidong Bing, Shafiq Joty, Luo Si

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

Challenge:	Existing methods for language model pretraining use limited knowledge graph data for knowledge-intensive tasks.
Approach:	They propose to make better use of multilingual annotations and language agnostic properties of KG triples for pretraining LMs.
Outcome:	The proposed models show significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks.

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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

Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning (2024.naacl-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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Huiming Wang, Zhaodonghui Li, Liying Cheng, De Wen Soh, Lidong Bing

ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)

Challenge:	Existing methods for training contrastive learning based sentence embedding models are largely influenced by the quality of sentence pairs.
Approach:	They propose a framework that decomposes LLMs into three stages for training . they propose to refine the generated content at these stages to ensure only high-quality sentence pairs are utilized to train a base contrastive learning model.
Outcome:	The proposed framework surpasses ChatGPT and ChatGPP in terms of performance.

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Liying Cheng, Dekun Wu, Lidong Bing, Yan Zhang, Zhanming Jie, Wei Lu, Luo Si

Structured Data in NLP

Challenge:	Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description.
Approach:	They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information.
Outcome:	The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information.

APE: Argument Pair Extraction from Peer Review and Rebuttal via Multi-task Learning (2020.emnlp-main)

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Liying Cheng, Lidong Bing, Qian Yu, Wei Lu, Luo Si

GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems (2022.acl-long)

Challenge:	Argument mining is an important research field that attracts growing attention in recent years.
Approach:	They propose a new task to extract argument pairs from peer review and rebuttal . they use an open review platform to analyze the contents, structure and connections .
Outcome:	The proposed task is based on a dataset of 4,764 fully annotated review-rebuttal passage pairs . it is able to detect argumentative propositions and extract argument pairs from the corpus .

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Bosheng Ding, Junjie Hu, Lidong Bing, Mahani Aljunied, Shafiq Joty, Luo Si, Chunyan Miao

Improving Question Generation With to the Point Context (D19-1)

Challenge:	Existing multilingual task-oriented dialogue datasets lack high-quality data curation due to the high expense and challenges of human annotation.
Approach:	They propose a method that generates a multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems.
Outcome:	The proposed method generates a large-scale multilingual ToD dataset globalized from an English ToD data set for three unexplored use cases of multilingual toD systems.

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Jingjing Li, Yifan Gao, Lidong Bing, Irwin King, Michael R. Lyu