Papers by Erik Cambria

Language Models as Inductive Reasoners (2024.eacl-long)

Challenge:	Existing charge prediction datasets focus on single-defendant cases, but real-world cases involve multiple defendants.
Approach:	They propose a benchmark that encompasses legal cases involving multiple defendants . they develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges.
Outcome:	The proposed model outperforms state-of-the-art models in predicting crime charges while providing corresponding rationales.

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Zonglin Yang, Li Dong, Xinya Du, Hao Cheng, Erik Cambria, Xiaodong Liu, Jianfeng Gao, Furu Wei

Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis (N18-2)

Challenge:	Inductive reasoning is a core component of human intelligence.
Approach:	They propose a task to induce natural language rules from natural language facts using natural language as representation for knowledge instead of formal language.
Outcome:	The proposed task surpasses baselines in both automatic and human evaluations.

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Devamanyu Hazarika, Soujanya Poria, Prateek Vij, Gangeshwar Krishnamurthy, Erik Cambria, Roger Zimmermann

Information Extraction & Text Mining

Challenge:	Present neural-based models exploit aspect and its contextual information in the sentence but ignore inter-aspect dependencies.
Approach:	They propose to combine aspect-based sentiment analysis with temporal dependency processing to incorporate this pattern into a sentence.
Outcome:	The proposed approach is based on the SemEval 2014 dataset and shows that it is effective for predicting sentiments of aspects in sentences with multiple aspects.

EmpathyEar: An Open-source Avatar Multimodal Empathetic Chatbot (2024.acl-demos)

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Hao Fei, Han Zhang, Bin Wang, Lizi Liao, Qian Liu, Erik Cambria

Dialogue Systems & Conversational Agents

Challenge:	EmpathyEar is an open-source, avatar-based multimodal empathetic chatbot . currently, ERG systems rely on text, sound, and vision .
Approach:	They propose an open-source, avatar-based multimodal empathetic chatbot to fill the gap in traditional text-only ERG systems.
Outcome:	The proposed system enables users to generate emotional responses to user queries . it can also generate avatars with talking faces and synchronized speeches .

MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)

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Rui Mao, Kai He, Claudia Ong, Qian Liu, Erik Cambria

Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective (2024.emnlp-main)

Challenge:	Existing methods for metaphor interpretation are slow due to lack of annotated datasets and effective pre-trained language models.
Approach:	They propose a large annotated dataset and a PLM for the metaphor interpretation task.
Outcome:	The proposed method improves on metaphor identification and interpretation with comparable baselines on the new dataset.

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Zhihao Zhang, Sophia Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

Information Extraction & Text Mining

Challenge:	Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP)
Approach:	They propose to automatically generate task-oriented knowledge using large language models (LLMs) and then employ task-orientated pre-training (TOPT) to facilitate domain adaptation.
Outcome:	The proposed model can learn to distinguish between different entities and improve its domain adaptation.

When Words Smile: Generating Diverse Emotional Facial Expressions from Text (2025.emnlp-main)

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Haidong Xu, Meishan Zhang, Hao Ju, Zhedong Zheng, Erik Cambria, Min Zhang, Hao Fei

Challenge:	Existing systems that generate only coarse facial expressions ignore the rich and dynamic nature of face-to-face communication.
Approach:	They propose an end-to-end text-to expression model that explicitly focuses on emotional dynamics.
Outcome:	The proposed model outperforms baselines on 15,000 text–3D expression pairs on a large-scale dataset.

Better and Worse with Scale: How Contextual Entrainment Diverges with Model Size (2026.findings-acl)

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Dikshant Kukreja, Kshitij Sah, Gautam Gupta, Avinash Anand, Rajiv Ratn Shah, Zhengkui Wang, Aik Beng Ng, Erik Cambria

Challenge:	Larger language models become better and worse at handling contextual information . et al. (2017) formalized contextual entrainment as a tendency to favor tokens in context .
Approach:	They formalize the first scaling laws for contextual entrainment . they find large models are four times more resistant to counterfactual misinformation .
Outcome:	The largest models are four times more resistant to counterfactual misinformation than the smallest, but twice as prone to copying arbitrary tokens.

MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations (P19-1)

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Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea

Dialogue Systems & Conversational Agents

Challenge:	Emotion recognition in conversations has gained popularity due to its potential applications. Until now, a large multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing.
Approach:	They propose to extend and enhance EmotionLines by combining 13,000 utterances from Friends dialogues with emotion and sentiment labels.
Outcome:	The proposed dataset contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends.

Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (2023.findings-emnlp)

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Wei Li, Luyao Zhu, Wei Shao, Zonglin Yang, Erik Cambria

Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings (2022.coling-1)

Challenge:	Existing discourse parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks.
Approach:	They propose to introduce a task-aware paradigm to improve the versatility of the parser.
Outcome:	Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the proposed framework.

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Sooji Han, Rui Mao, Erik Cambria

Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026.findings-acl)

Challenge:	Existing black-box-like deep learning methods for depression detection focus on improving classification performance, but it is impossible to explain and interpret those models that rely on state-of-the-art (SOTA) deep learning techniques.
Approach:	They propose to use hierarchical attention mechanisms and feed-forward neural networks to encode a model for depression detection on Twitter that leverages metaphorical concept mappings as input.
Outcome:	The proposed model leverages metaphorical concept mappings as input to detect depressed individuals and identify features of such users’ tweets.

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Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, Erik Cambria

Low-Resource NLP

Challenge:	Existing large language models are limited in understanding, reasoning, calculation, and generation, limiting their performance in complex reasoning and dynamic tasks.
Approach:	They propose a plug-and-play framework that integrates a small-scale LLM (as agent) with large-scale large-level LLMs (a as environment) they propose generating prompts that are used to interact with LLM, and a double constraint reward that optimizes correctness and quality of generation.
Outcome:	The proposed framework significantly outperforms baseline large-scale large-language models across various tasks.

Large Language Models for Automated Open-domain Scientific Hypotheses Discovery (2024.findings-acl)

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Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, Erik Cambria

Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization (2025.acl-long)

Challenge:	Existing research on hypothetical induction is limited by the observation annotations in the dataset and the ground truth hypotheses are mostly commonsense knowledge.
Approach:	They propose a first dataset for social science academic hypotheses discovery using raw web corpus as observations and propose valid, useful scientific hypothese . they propose 'a multi-module framework' that includes feedback mechanisms to boost performance.
Outcome:	The proposed dataset generates valid, novel, and helpful scientific hypotheses, even new to humanity, using open-domain data and a web corpus as observations.

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Keane Ong, Rui Mao, Deeksha Varshney, Erik Cambria, Gianmarco Mengaldo

Robustness in NLP

Challenge:	Existing NLP methods lack robustness against greenwashed ESG content . existing methods often extract insights that reflect misleading or exaggerated sustainability claims rather than objective ESG performance.
Approach:	They propose to use a dataset to improve the robustness of ESG analysis amid the prevalence of greenwashing to analyze sustainability reports.
Outcome:	The proposed model improves robustness against greenwashed claims rather than objective ESG performance.

SEA-BED: How Do Embedding Models Represent Southeast Asian Languages? (2026.acl-long)

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Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng, Jann Railey Montalan, Thura Aung, Jian Gang Ngui, Yosephine Susanto, William Chandra Tjhi, Panuthep Tasawong, Erik Cambria, Ekapol Chuangsuwanich, Sarana Nutanong

Representation Learning

Challenge:	SEA-BED examines how multilingual text embeddings perform across tasks and languages . performance gaps arise from data coverage, training objectives, and architectural design, authors say .
Approach:	They propose a large-scale benchmark covering 10 SEA languages and diverse embedding tasks.
Outcome:	The proposed model performs poorly across languages and tasks, but language-task analyses reveal inconsistencies . the results suggest that performance gaps arise from limitations in data coverage, training objectives, and architectural design.

Reasoning with Trees: Faithful Question Answering over Knowledge Graph (2025.coling-main)

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Tiesunlong Shen, Jin Wang, Xuejie Zhang, Erik Cambria

Knowledge Representation

Challenge:	Recent advances in large language models (LLMs) have shown remarkable progress in reasoning capabilities, yet they still face challenges in complex, multi-step reasoning tasks.
Approach:	They propose a framework that synergistically integrates LLMs with knowledge graphs (KGs) to enhance reasoning performance and interpretability.
Outcome:	The proposed framework outperforms existing state-of-the-art methods on two benchmark KGQA datasets and improves on the MCTS process.

Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (P19-1)

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Wei Zhao, Haiyun Peng, Steffen Eger, Erik Cambria, Min Yang

Challenge:	Existing approaches to generalize deep neural networks are datahungry and generalize poorly from small datasets.
Approach:	They propose an agreement score to evaluate routing processes at instance-level and an adaptive optimizer to enhance routing.
Outcome:	The proposed approach improves on two NLP tasks and in low-resource settings with few training instances.

Understanding Refusal in Language Models with Sparse Autoencoders (2025.findings-emnlp)

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Wei Jie Yeo, Nirmalendu Prakash, Clement Neo, Ranjan Satapathy, Roy Ka-Wei Lee, Erik Cambria

Prompted Aspect Key Point Analysis for Quantitative Review Summarization (2024.acl-long)

Challenge:	a study of refusal in instruction-tuned language models identifies latent features that causally mediate refusal behaviors.
Approach:	They conduct a mechanistic study of refusal in instruction-tuned LLMs using sparse autoencoders . they identify latent features that causally mediate refusal behaviors using sparsed autoencoding .
Outcome:	The proposed method validates refusal-related features across multiple datasets.

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An Tang, Xiuzhen Zhang, Minh Dinh, Erik Cambria

Summarization

Challenge:	Recent abstractive approaches generate KPs based on sentences, resulting in overlapping and hallucinated opinions.
Approach:	They propose to use supervised learning to extract short sentences as key points before matching them to review comments for quantification of KP prevalence.
Outcome:	The proposed framework achieves state-of-the-art performance on Yelp and SPACE.

ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (D18-1)

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Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann

IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment Analysis (D18-1)

Challenge:	Existing studies do not explicitly consider inter-personal influences that thrive in the emotional dynamics of dialogues.
Approach:	They propose a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories.
Outcome:	The proposed model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.

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Navonil Majumder, Soujanya Poria, Alexander Gelbukh, Md. Shad Akhtar, Erik Cambria, Asif Ekbal

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

Challenge:	Aspect-based sentiment analysis is a new approach to extract aspect specific sentimental information from user feedback.
Approach:	They propose a method that incorporates neighboring aspects related information into the sentiment classification of a target aspect using memory networks.
Outcome:	The proposed method outperforms the state-of-the-art by 1.6% on average in restaurant and laptop domains.

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

Multilinguality

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.

Neuro-Symbolic Sentiment Analysis with Dynamic Word Sense Disambiguation (2023.findings-emnlp)

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Xulang Zhang, Rui Mao, Kai He, Erik Cambria

Explainability & Interpretability in NLP

Challenge:	Traditional neural network models represent word senses as vectors that are uninterpretable for humans.
Approach:	They propose a framework that incorporates word Sense Disambiguation (WSD) by identifying and paraphrasing ambiguous words to improve sentiment predictions.
Outcome:	The proposed framework improves sentiment analysis accuracy and interpretability on a downstream task without ground-truth word sense labels.

The Plagiarism Singularity Conjecture (2025.naacl-long)

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Sriram Ranga, Rui Mao, Erik Cambria, Anupam Chattopadhyay

SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset (2024.lrec-main)

Challenge:	Large language models (LLMs) are being used to generate content at an unprecedented scale, raising concerns over their misuse and saturation of the content space with artificially generated material.
Approach:	They propose to use large language models to generate text that looks indistinguishable from that written by humans.
Outcome:	The proposed model can generate 10-30 sentences to breach the plagiarism limit, the authors estimate .

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Tan Yue, Xuzhao Shi, Rui Mao, Zonghai Hu, Erik Cambria

Plausible Extractive Rationalization through Semi-Supervised Entailment Signal (2024.findings-acl)

Challenge:	Sarcasm is an implicit form of sarcasm, involving an intended meaning that contradicts the literal expression . human use conflict between factual information and a statement as cues to detect sarcasm . sarkasmatic analysis is challenging due to its implicit nature .
Approach:	They propose a multimodal sarcasm detection dataset that uses multiple modalities to detect sarcasm.
Outcome:	The proposed model improves on previous models based on a single label . human sarcasm cannot be detected using a unified label across multiple modalities .

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Yeo Wei Jie, Ranjan Satapathy, Erik Cambria

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

Challenge:	Abstract: Large language models are gaining widespread adoption in natural language processing tasks.
Approach:	They propose a semi-supervised approach to optimize for plausibility of extracted rationales by using a pre-trained natural language inference model and a supervised NLI predictor.
Outcome:	The proposed model outperforms unsupervised models by > 100% on a ERASER dataset.

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

Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation (2025.emnlp-main)

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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Keane Ong, Rui Mao, Deeksha Varshney, Paul Pu Liang, Erik Cambria, Gianmarco Mengaldo

ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition (2026.findings-acl)

Challenge:	Existing methods for forward counterfactual generation face limitations . large language models (LLMs) offer promise but remain unexplored for this application .
Approach:	They propose a benchmark to support forward counterfactual generation in finance . they use financial news headlines to curate financial news and provide structured evaluation .
Outcome:	The proposed benchmark aims to provide scalable, automated insights into potential market opportunities and risks for stakeholders.

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Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni, Ben Gao, Yuqiang Li, Shixiang Tang, Wanli Ouyang, Erik Cambria, Dongzhan Zhou

TECHS: Temporal Logical Graph Networks for Explainable Extrapolation Reasoning (2023.acl-long)

Challenge:	Large language models have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark.
Approach:	They propose a benchmark for evaluating large language models on a sufficient set of scientific discovery sub-tasks.
Outcome:	The proposed framework extracts critical components from papers across 12 disciplines with expert validation confirming its accuracy.

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Qika Lin, Jun Liu, Rui Mao, Fangzhi Xu, Erik Cambria

MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2022.lrec-1)

Challenge:	Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge.
Approach:	They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer.
Outcome:	The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts.

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Shaoxiong Ji, Tianlin Zhang, Luna Ansari, Jie Fu, Prayag Tiwari, Erik Cambria

Towards Faithful Natural Language Explanations: A Study Using Activation Patching in Large Language Models (2025.emnlp-main)

Challenge:	Existing pretrained language models for mental health detection are inadequate . one in four people worldwide suffers from mental disorders .
Approach:	They train and release two pretrained masked language models to benefit machine learning for mental healthcare research . they demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks.
Outcome:	The proposed models improve mental health detection tasks on several benchmarks and are available for free.

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Wei Jie Yeo, Ranjan Satapathy, Erik Cambria

Explainability & Interpretability in NLP

Challenge:	Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) however, the faithfulness of these explanations should not be readily trusted at face value.
Approach:	They propose to use a causal mediation technique called activation patching to measure the faithfulness of an explanation towards supporting the explained answer.
Outcome:	The proposed metric, Causal Faithfulness, quantifies the consistency of causal attributions between explanations and the corresponding model outputs as the indicator of faithfulness.

A Comprehensive Graph Framework for Question Answering with Mode-Seeking Preference Alignment (2025.findings-acl)

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Quanwei Tang, Sophia Yat Mei Lee, Junshuang Wu, Dong Zhang, Shoushan Li, Erik Cambria, Guodong Zhou

Structured Data in NLP

Challenge:	Existing studies struggle with achieving global understanding of large language models . GraphMPA is a graph-based framework with mode-seeking preference alignment .
Approach:	They propose a graph-based framework with mode-seeking preference alignment to improve model outputs.
Outcome:	The proposed framework constructs a hierarchical document graph mimicking human cognitive processes for information understanding and synthesis.

Vanessa: Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis (2024.findings-emnlp)

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Luwei Xiao, Rui Mao, Xulang Zhang, Liang He, Erik Cambria

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

Challenge:	Existing methods to analyze images focus on superficial features or descriptions, omitting subtle contextual information.
Approach:	They propose a Visual Connotation and Aesthetic Attributes Understanding Network (Vanessa) for Multimodal Aspect-based Sentiment Analysis.
Outcome:	The proposed network captures both implicit and explicit sentimental cues and can be used to enrich textual sentiment analysis.

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

Information Extraction & Text Mining

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.

Self-training Large Language Models through Knowledge Detection (2024.findings-emnlp)

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Yeo Wei Jie, Teddy Ferdinan, Przemyslaw Kazienko, Ranjan Satapathy, Erik Cambria

SenticVec: Toward Robust and Human-Centric Neurosymbolic Sentiment Analysis (2024.findings-acl)

Challenge:	Large language models (LLMs) often require extensive labeled datasets and training compute to achieve impressive performance across downstream tasks.
Approach:	They propose a self-training paradigm where the LLM curates its own labels and selectively trains on unknown data samples identified through a reference-free consistency method.
Outcome:	The proposed model reduces the dependency on large labeled datasets and mitigates catastrophic forgetting in out-of-distribution benchmarks.

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Xulang Zhang, Rui Mao, Erik Cambria

Robustness in NLP

Challenge:	Existing approaches to tackle learning challenges such as knowledge forgetting and extensive computing resources are not effective.
Approach:	They propose a novel neurosymbolic method for sentiment analysis that places emphasis on human subjectivity within varying domain annotations.
Outcome:	The proposed method is lightweight, robust across domains and languages, efficient few-shot training, and rapid convergence.

GPTEval: A Survey on Assessments of ChatGPT and GPT-4 (2024.lrec-main)

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Rui Mao, Guanyi Chen, Xulang Zhang, Frank Guerin, Erik Cambria

Dialogue Systems & Conversational Agents

Challenge:	emergence of ChatGPT has generated speculation about its potential to disrupt social and economic systems.
Approach:	They analyze prior assessments of ChatGPT and GPT-4 to analyze their language and reasoning abilities, scientific knowledge, ethical considerations and existing evaluation methods.
Outcome:	The proposed model performs satisfactorily in science knowledge and can answer open questions.

PAED: Zero-Shot Persona Attribute Extraction in Dialogues (2023.acl-long)

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Luyao Zhu, Wei Li, Rui Mao, Vlad Pandelea, Erik Cambria

Low-Resource NLP

Challenge:	Existing methods for persona attribute extraction from conversations are inconsistent and unreliable.
Approach:	They propose a model with a hard negative sampling strategy for generalized zero-shot persona attribute extraction.
Outcome:	The proposed model outperforms existing models in persona attribute extraction tasks.

Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets (2020.coling-main)

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Frank Xing, Lorenzo Malandri, Yue Zhang, Erik Cambria

End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion (2023.eacl-main)

Challenge:	Recent dominance of machine learning-based natural language processing methods has overemphasized model accuracies rather than studying the reasons behind their errors.
Approach:	They investigate the error patterns of some widely acknowledged sentiment analysis methods in the finance domain.
Outcome:	The proposed models are based on the existing models and have important clues for improving them.

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Zonglin Yang, Xinya Du, Erik Cambria, Claire Cardie

MetaPro Online: A Computational Metaphor Processing Online System (2023.acl-demo)

Challenge:	Pretrained language models have been shown to store knowledge in their parameters and have achieved reasonable performance in knowledge-intensive tasks.
Approach:	They propose to provide retrieved passages that contain relevant knowledge as additional input to the commonsense knowledge base completion (CKBC) task.
Outcome:	The proposed framework generates more valid, informative, and novel knowledge than the state-of-the-art COMET model for commonsense knowledge base completion (CKBC) tasks.

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Rui Mao, Xiao Li, Kai He, Mengshi Ge, Erik Cambria

Challenge:	Metaphors do not take literal meanings in contexts, which may cause difficulties for language learners and machines to understand them.
Approach:	They propose a computational metaphor processing online system that queries metaphoricity labels, paraphrases and concept mappings for non-domain-specific text.
Outcome:	The proposed system can query metaphoricity labels, paraphrases, and concept mappings for non-domain-specific text without coding background.

Finding the Pillars of Strength for Multi-Head Attention (2023.acl-long)

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Jinjie Ni, Rui Mao, Zonglin Yang, Han Lei, Erik Cambria

IRIS: Interleaved Reinforcement with Incremental Staged Curriculum for Cross-Lingual Mathematical Reasoning (2026.acl-long)

Challenge:	Recent studies have revealed some issues of Multi-Head Attention (MHA) e.g., redundancy and over-parameterization.
Approach:	They propose to train attention heads with a self-supervised group constraint to focus on an essential but distinctive feature subset.
Outcome:	The proposed method achieves significant performance gains on three well-established tasks while significantly compressing parameters.

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Navya Gupta, Rishitej Reddy Vyalla, Avinash Anand, Chhavi Kirtani, Erik Cambria, Zhengchen Zhang, Zhengkui Wang, Timothy Liu, Aik Beng Ng, Simon See, Rajiv Ratn Shah

Multilinguality

Challenge:	Curriculum learning fails to generate consistent step-by-step reasoning in multilingual and low-resource settings.
Approach:	They propose a framework that combines supervised fine-tuning with reverse curriculum reinforcement learning to generate consistent step-by-step reasoning.
Outcome:	The proposed framework outperforms single-axis benchmarks and multilingual test sets on math reasoning tasks and in high-resource languages.

CASCADE: Contextual Sarcasm Detection in Online Discussion Forums (C18-1)

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Devamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, Erik Cambria, Roger Zimmermann, Rada Mihalcea

LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence (2026.findings-acl)

Challenge:	Existing studies on sarcasm detection focus on lexical, syntactic and semantic cues, but sarcasm can be expressed implicitly without such cue.
Approach:	They propose a ContextuAl SarCasm DEtector which extracts contextual information from the discourse of a discussion thread.
Outcome:	The proposed model improves on a large Reddit corpus.

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Wenjin Liu, Haoran Luo, Xin Feng, Xiang Ji, Lijuan Zhou, Rui Mao, Jiapu Wang, Shirui Pan, Erik Cambria

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (P18-1)

Challenge:	Existing benchmarks for legal general intelligence (GI) are result-oriented and do not evaluate the legal intelligence of large language models (LLMs).
Approach:	They propose a Chinese legal benchmark for evaluating legal GI in large language models . they use recent legal cases and exam questions to create multiple-choice questions .
Outcome:	The proposed benchmarks lack a systematic evaluation of the legal intelligence of large language models (LLMs) the results show that even the best LLMs lagging behind human legal professionals.

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AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cambria, Louis-Philippe Morency

SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis (2022.lrec-1)

Challenge:	Analyzing human multimodal language is emerging area of research in NLP.
Approach:	They propose a multimodal fusion technique to exploit how modalities interact in multimodal language.
Outcome:	The proposed technique exploits how modalities interact with each other in human multimodal language.

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Erik Cambria, Qian Liu, Sergio Decherchi, Frank Xing, Kenneth Kwok