Papers by Ruihong Huang

A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing (D19-1)

Challenge:	lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization.
Approach:	They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias.
Outcome:	The proposed model shows that informational bias appears more frequently than lexical bias.

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Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics (2021.findings-emnlp)

Challenge:	Existing approaches to discourse parsing use commonsense knowledge and linguistic constraints to integrate them into neural network models.
Approach:	They propose a knowledge regularization approach that integrates linguistic constraints with contexts for deriving word representations.
Outcome:	The proposed approach outperforms previous systems on the benchmark dataset PDTB for discourse parsing.

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Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event (2020.acl-main)

Discourse & Pragmatics

Challenge:	Experimental results show that the hierarchical model learns to segment a document into subtopics and improves performance on the news discourse profiling task.
Approach:	They propose a hierarchical neural network that models multi-level interaction between sentences, subtopics, and the document.
Outcome:	The proposed model outperforms the existing model on the news discourse profiling task.

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Prafulla Kumar Choubey, Aaron Lee, Ruihong Huang, Lu Wang

Challenge:	a recent study shows that news articles report context-informing content that is not necessarily relevant to main events.
Approach:	They propose to use a functional discourse structure for news articles to model news content structures . they propose to integrate system predicted news structures into the annotations .
Outcome:	The proposed model outperforms existing models in event coreference resolution.

Hierarchical Fusion for Online Multimodal Dialog Act Classification (2023.findings-emnlp)

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Md Messal Monem Miah, Adarsh Pyarelal, Ruihong Huang

Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)

Challenge:	Existing multimodal DA classification approaches are limited by ineffective audio modeling and late-stage fusion.
Approach:	They propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances.
Outcome:	The proposed model achieves a significant increase in the F1 score relative to current state-of-the-art models on two prominent DA classification datasets, MRDA and EMOTyDA.

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Lei Gao, Prafulla Kumar Choubey, Ruihong Huang

Challenge:	a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations .
Approach:	They propose to identify all event causal relations in a document, both within a sentence and across sentences.
Outcome:	The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations .

Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree (2024.emnlp-main)

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Hidden in Plain Sight: Evaluation of the Deception Detection Capabilities of LLMs in Multimodal Settings (2025.acl-long)

Challenge:	Logical fallacy is the use of invalid or flawed reasoning in the construction of a statement.
Approach:	They propose to build a logical structure tree to represent hierarchical logic flow among relation connectives and their arguments in a statement.
Outcome:	The proposed model significantly improves accuracy and recall for fallacy detection and fallacy classification.

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Md Messal Monem Miah, Adrita Anika, Xi Shi, Ruihong Huang

MultiCAT: Multimodal Communication Annotations for Teams (2025.findings-naacl)

Challenge:	Detecting deception in an increasingly digital world is a critical and challenging task.
Approach:	They evaluate the performance of both open-source and proprietary LLMs on three datasets . they find that fine-tuned LLM achieve state-of-the-art performance on textual deception detection .
Outcome:	The proposed models achieve state-of-the-art on textual deception detection, whereas LMMs struggle to fully leverage multimodal cues.

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Adarsh Pyarelal, John M Culnan, Ayesha Qamar, Meghavarshini Krishnaswamy, Yuwei Wang, Cheonkam Jeong, Chen Chen, Md Messal Monem Miah, Shahriar Hormozi, Jonathan Tong, Ruihong Huang

Automatic Data Acquisition for Event Coreference Resolution (2021.eacl-main)

Challenge:	Recent flagship models from OpenAI and Google are only capable of 1-on-1 interactions with humans, limiting the potential for integration into human-machine teams of the future.
Approach:	They propose a dataset that allows team members to make multiple types of predictions on the same dataset.
Outcome:	The proposed dataset builds upon data from teams working collaboratively to save victims in a simulated search and rescue mission.

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Temporal Event Knowledge Acquisition via Identifying Narratives (P18-1)

Challenge:	lexical paraphrases and high precision rules informed by news discourse structure can be used to collect coreferential and non-coreferential event pairs from unlabeled English news articles.
Approach:	They propose to use lexical paraphrases and news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles.
Outcome:	The proposed model performs better than the supervised model on evaluation datasets with different event domains and text genres.

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Wenlin Yao, Ruihong Huang

Challenge:	Existing knowledge of narrative examples is lacking and difficult to obtain.
Approach:	They propose a weakly supervised approach for acquiring rich temporal event knowledge across sentences in narrative stories.
Outcome:	The proposed approach outperforms neural network models on the narrative cloze task.

Weakly Supervised Subevent Knowledge Acquisition (2020.emnlp-main)

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Wenlin Yao, Zeyu Dai, Maitreyi Ramaswamy, Bonan Min, Ruihong Huang

All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison (2023.emnlp-main)

Challenge:	Subevents elaborate an event and exist in event descriptions.
Approach:	They propose a weakly supervised approach to extract subevent relation tuples from text . they then use the initial seed subeven pairs to train a contextual classifier .
Outcome:	The proposed method is high quality and covers a wide range of event types.

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Yujian Liu, Xinliang Zhang, Kaijian Zou, Ruihong Huang, Nicholas Beauchamp, Lu Wang

One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words (2020.lrec-1)

Challenge:	a recent study shows that media influence opinion via the inclusion or omission of partisan events.
Approach:	They develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology.
Outcome:	The proposed framework validates the existence of partisan event selection and detects partisan events and article ideology better than baselines.

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MMR-GRPO: Accelerating GRPO-Style Training through Diversity-Aware Reward Reweighting (2026.findings-acl)

Challenge:	Existing word-specific classifiers lack the ability to generalize across words and require limited sense-annotated data for every word.
Approach:	They propose to learn a single model that derives sense representations and enforces congruence between a word instance and its right sense by using both sense-annotated data and lexical resources.
Outcome:	Empirical evaluation shows the proposed model outperforms classifier-based models by 1.7%, 2.5% and 3.8% in F1-score on GloVe, ELMo and BERT word embeddings respectively.

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Kangda Wei, Ruihong Huang

Reasoning

Challenge:	Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in mathematical reasoning tasks.
Approach:	They propose to use Maximal Marginal Relevance to reweigh rewards of multiple rollouts by balancing rollout quality with diversity to reduce rollout redundancy.
Outcome:	The proposed approach reduces training time and costs by 47.9% . evaluations across three model sizes, three GRPO variants, and five mathematical reasoning benchmarks show that it achieves comparable peak performance while requiring on average 70.2% less wall-clock time.

Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation (2025.acl-long)

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Improving Dialogue State Tracking by Discerning the Relevant Context (N19-1)

Challenge:	a recent study has focused on detecting media bias in news articles . a multi-document event relation graph is used to generate a neutralized summary .
Approach:	They propose to generate a neutralized summary given multiple articles presenting different ideological views.
Outcome:	The proposed method mitigates media bias and improves content preservation.

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Sanuj Sharma, Prafulla Kumar Choubey, Ruihong Huang

Dialogue Systems & Conversational Agents

Challenge:	Dialog state tracking (DST) is used to estimate user's goals and requests in order to plan next action and respond accordingly.
Approach:	They propose a framework that uses the current user utterance and the most recent system utterant to determine the relevance of a system . Specifically, they use the current and most recent user . and system adverbs to determine relevance.
Outcome:	The proposed framework improves goal accuracy by 2.75% and 2.36% on WoZ 2.0 and Multi-WoZ restaurant domain datasets over the previous state-of-the-art GLAD model.

Composition-contrastive Learning for Sentence Embeddings (2023.acl-long)

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Sachin Chanchani, Ruihong Huang

OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions (2026.eacl-long)

Challenge:	Recent work shows potential to learn vector representations from unlabelled data without task-specific fine-tuning.
Approach:	They propose to maximize alignment between textual embeddings and a composition of their phrasal constituents.
Outcome:	The proposed approach improves on similarity tasks comparable to state-of-the-art approaches.

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Yu-Shin Huang, Peter Just, Hanyun Yin, Krishna Narayanan, Ruihong Huang, Chao Tian

Language Models

Challenge:	In coverless steganography, secret bits are embedded in as few language tokens as possible . stego-texts can be decoded by eavesdroppers, but are difficult to detect .
Approach:	They propose a method to embed secret bits in language tokens using a Large Language Model . they propose maximizing the entropy of a replacement probability distribution .
Outcome:	The proposed method should embed secret bits in as few language tokens as possible while keeping the stego-text as natural as possible.

Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure (2022.acl-short)

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Bohan Zhang, Prafulla Kumar Choubey, Ruihong Huang

Text Generation

Challenge:	Document-level text simplification often deletes some sentences to reduce text complexity.
Approach:	They use a news genre-specific functional discourse structure to predict sentence deletions . they incorporate sentence categories into a neural net model to improve recall .
Outcome:	The proposed model improves the recall of deletion prediction by 6.5% and 10.7%, and the overall F1 score by 3.6% and 4.3% respectively.

PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge (2020.emnlp-main)

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Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, James Caverlee

Text Generation

Challenge:	Paraphrase identification requires specialized domain knowledge to perform . state-of-the-art neural models and non-expert human annotators have poor performance on PARADE .
Approach:	They propose a benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
Outcome:	The proposed dataset shows state-of-the-art models and non-expert human annotators have poor performance on PARADE.

Are LLMs Good Annotators for Discourse-level Event Relation Extraction? (2024.findings-emnlp)

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Kangda Wei, Aayush Gautam, Ruihong Huang

Evaluating Gender Bias of LLMs in Making Morality Judgements (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks, but their effectiveness over discourse-level event relation extraction tasks remains unexplored.
Approach:	They evaluate LLMs' ability to address discourse-level event relation extraction tasks using an open-source model and a commercial model.
Outcome:	The proposed model performs poorly on discourse-level event relation extraction tasks.

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Divij Bajaj, Yuanyuan Lei, Jonathan Tong, Ruihong Huang

Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in a multitude of NLP tasks, but are still not immune to limitations such as gender bias.
Approach:	They propose to use a dataset to examine whether LLMs possess gender bias when asked to give moral opinions.
Outcome:	The proposed models show that they are biased when asked to give moral opinions.

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Danny Wang, Ruihong Qiu, Guangdong Bai, Zi Huang

Challenge:	Existing methods for out-of-distribution (OOD) detection ignore textual-structural diversity . text-rich networks (TrNs) represent complex interplay between textual content and relational structures .
Approach:	They propose a framework for evaluating out-of-distribution detection in text-rich networks . they propose augmentations, structural shifts, and domain-based divisions to model interplay .
Outcome:	Experiments on 11 datasets show the framework is effective in out-of-distribution detection.

Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations (N18-2)

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Prafulla Kumar Choubey, Kaushik Raju, Ruihong Huang

LegalCore: A Dataset for Event Coreference Resolution in Legal Documents (2025.findings-acl)

Challenge:	Identifying the most dominant and central event of a document is useful for many applications, says a new study . identifying the most prominent event in a news article is useful in text summarization, storyline generation and text segmentation.
Approach:	They propose to detect the most dominant and central event of a document . central event usually has many coreferential event mentions scattered throughout document a .
Outcome:	The proposed task can detect the most dominant and central event in a document . it can be used for text summarization, storyline generation and text segmentation .

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Kangda Wei, Xi Shi, Jonathan Tong, null Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, Ruihong Huang

Reconstructing Event Regions for Event Extraction via Graph Attention Networks (2020.aacl-main)

Challenge:	Existing research on event coreference resolution is limited to news articles . existing datasets for news articles are limited to events and coreferences .
Approach:	They present a dataset for the legal domain LegalCore which has been annotated with event and event coreference information.
Outcome:	The legal contract documents annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document.

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Pei Chen, Hang Yang, Kang Liu, Ruihong Huang, Yubo Chen, Taifeng Wang, Jun Zhao

EMONA: Event-level Moral Opinions in News Articles (2024.naacl-long)

Challenge:	Existing approaches for event extraction focus on sentence-level event extraction, but they lack a broader view of the document context.
Approach:	They build graphs with candidate event filler extractions enriched by sentential embeddings as nodes and use graph attention networks to identify event regions in a document and aggregate event information.
Outcome:	The proposed method performs well on two languages and shows that it is faster than previous methods.

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Yuanyuan Lei, Md Messal Monem Miah, Ayesha Qamar, Sai Ramana Reddy, Jonathan Tong, Haotian Xu, Ruihong Huang

Sentiment Analysis

Challenge:	Recent work on news articles has focused on social media short texts, but little has explored moral sentiment within news articles.
Approach:	They propose to extract event-level moral opinions from news articles using a new dataset . they use annotated event-based moral opinions to analyze news articles .
Outcome:	The proposed dataset consists of 400 news articles containing over 10k sentences and 45k events, among which 9,613 events received moral foundation labels.

Identifying Conspiracy Theories News based on Event Relation Graph (2023.findings-emnlp)

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Discourse Structures Guided Fine-grained Propaganda Identification (2023.emnlp-main)

Challenge:	Conspiracy theories are narratives that explains an event or situation in an irrational or malicious manner.
Approach:	They propose to integrate an event relation graph into conspiracy theory identification by using soft labels.
Outcome:	The proposed approach improves precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources.

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A Joint Model for Structure-based News Genre Classification with Application to Text Summarization (2021.findings-acl)

Challenge:	Using teacher-predicted probabilities and knowledge distillation frameworks to identify propaganda content is important.
Approach:	They propose to integrate local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively.
Outcome:	The proposed models improve accuracy and recall of propaganda content identification at sentence-level and token-level.

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Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph (N18-1)

Challenge:	Existing models for structure-based news genre classification identify news structure types and news elements . authors show that the model outperforms variants that perform two tasks independently .
Approach:	They propose a joint model that identifies one of four commonly used news structures for a news article and recognizes a sequence of news elements within the article that define the corresponding news structure.
Outcome:	The proposed model outperforms variants that perform two tasks independently . it predicts news structure type and news elements and improves text summarization .

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Building Context-aware Clause Representations for Situation Entity Type Classification (D18-1)

Text Classification

Challenge:	Existing methods for predicting implicit discourse relations ignore wider paragraph contexts beyond the two discourse units examined for a discourse relation prediction.
Approach:	They propose a paragraph-level neural network that models inter-dependencies between discourse units and discourse relation continuity and patterns and predicts a sequence of discourse relations in a sentence.
Outcome:	The proposed model outperforms state-of-the-art systems on the benchmark corpus of PDTB.

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Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs (2025.findings-emnlp)

Challenge:	Existing models for categorizing clauses based on situation entity types do not provide accurate results.
Approach:	They propose to build context-aware clause representations for predicting situation entity types of clauses by modeling context influences and inter-dependencies of clause.
Outcome:	The proposed model achieves state-of-the-art performance on genre-rich dataset MASC+Wiki .

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Kangda Wei, Hasnat Md Abdullah, Ruihong Huang

Who is Speaking? Speaker-Aware Multiparty Dialogue Act Classification (2023.findings-emnlp)

Challenge:	Large Language Models often exhibit gender bias, resulting in unequal treatment of male and female subjects across contexts.
Approach:	They propose a framework that encourages exploratory thinking in large language models . the framework generates story pairs featuring male and female protagonists in structurally identical scenarios .
Outcome:	The proposed framework reduces gender bias while preserving or even enhancing general model capabilities.

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Ayesha Qamar, Adarsh Pyarelal, Ruihong Huang

Information Retrieval

Challenge:	Identifying how speakers interact with each other in a conversation is difficult when more than two interlocutors take part in . To overcome this challenge, we propose to explicitly add speaker awareness to each utterance representation.
Approach:	They propose to add speaker awareness to each utterance representation to model how each speaker is behaving within the local context of a conversation.
Outcome:	The proposed approach is able to model multiparticipant and dyadic conversations on the MRDA and SwDA datasets and shows that it is more efficient than previous approaches.

Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures (P18-1)

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Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs (2022.aacl-short)

Challenge:	a novel approach for event coreference resolution models correlations between event chains and document topical structures.
Approach:	They propose a novel approach that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation.
Outcome:	The proposed approach improves performance across a dataset of document topics . it shows that the models can identify and link event mentions that refer to the same event .

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Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition (2022.naacl-main)

Structured Data in NLP

Challenge:	Using news discourse profiling, we can identify temporal relationships between events and time expressions that are temporally related and otherwise difficult to locate.
Approach:	They propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs.
Outcome:	The proposed model can identify distant inter-sentence event and (or) time expression pairs that are temporally related and otherwise difficult to locate.

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Pei Chen, Haotian Xu, Cheng Zhang, Ruihong Huang

Do LLMs Understand Dialogues? A Case Study on Dialogue Acts (2025.acl-long)

Challenge:	Named Entity Recognition (NER) datasets annotate coarse-grained entities such as a continent, a country, or a city.
Approach:	They propose a dataset HarveyNER with fine-grained locations annotated in tweets that characterizes many complex and long location mentions in informal descriptions.
Outcome:	The proposed dataset outperforms existing systems on hard cases and improves on the heuristic curricula.

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Ayesha Qamar, Jonathan Tong, Ruihong Huang

Dialogue Systems & Conversational Agents

Challenge:	Large Language Models (LLMs) have shown remarkable performance on many unseen tasks in a zero-shot setting.
Approach:	They propose to identify three key pre-tasks essential for accurate DA prediction: Turn Management, Communicative Function Identification, and Dialogue Structure Prediction.
Outcome:	The proposed model fails to outperform basic rule-based tasks on three key pre-tasks, and the results suggest that the model is flawed.

Sentence-level Media Bias Analysis Informed by Discourse Structures (2022.emnlp-main)

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Yuanyuan Lei, Ruihong Huang, Lu Wang, Nick Beauchamp

Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition (2021.acl-short)

Challenge:	Recent work on detecting media bias at the level of individual articles is limited to single sentences.
Approach:	They propose to use a news discourse structure and PDTB discourse relations to identify bias sentences within an article that can illuminate and explain the overall bias of the entire article.
Outcome:	The proposed model can detect bias at the level of individual articles and a single sentence can explain it.

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Pei Chen, Haibo Ding, Jun Araki, Ruihong Huang

Sentence-level Media Bias Analysis with Event Relation Graph (2024.naacl-long)

Challenge:	Named entity recognition (NER) is well studied for the general domain, but the performance is still moderate for specialized domains.
Approach:	They propose to explicitly connect entity mentions based on global coreference relations and local dependency relations to build better entity mention representations.
Outcome:	The proposed system improves the NER performance even with a tiny amount of labeled data.

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CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning (2026.acl-long)

Challenge:	Existing studies on media bias at the article level have identified media biases but only a few have been done on article level.
Approach:	They propose to construct an event relation graph to explicitly reason about event-event relations for sentence-level bias identification.
Outcome:	The proposed model improves both precision and recall of bias sentence identification.

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Xiangjue Dong, Cong Wang, Maria Teleki, Millennium Bismay, Ruihong Huang, James Caverlee

Robustness in NLP

Challenge:	Persona-assigned Large Language Models can be useful for personalized, context-aware reasoning.
Approach:	They propose a framework that harmonizes demographic perturbations into a unified prediction by balancing agreement and divergence among counterfactual personas.
Outcome:	The proposed framework improves reasoning performance even when base personas are suboptimal.

Few-Shot (Dis)Agreement Identification in Online Discussions with Regularized and Augmented Meta-Learning (2022.findings-emnlp)

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