Papers by Gabriele Pergola
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)
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| Challenge: | Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data. |
| Approach: | They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies. |
| Outcome: | The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives. |
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon. |
| Approach: | They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts . |
| Outcome: | The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% . |
Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection (2021.acl-long)
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| Challenge: | Emotion detection in dialogues requires the identification of thematic topics underlying a conversation, commonsense knowledge, and the intricate transition patterns between affective states. |
| Approach: | They propose a Topic-Driven Knowledge-Aware Transformer model that integrates topic representation and commonsense knowledge from ATOMIC for dialogue emotion detection. |
| Outcome: | The proposed model outperforms state-of-the-art models on four dialogue datasets . it can detect topics which help distinguish emotion categories, the authors show . |
DrugWatch: A Comprehensive Multi-Source Data Visualisation Platform for Drug Safety Information (2024.acl-demos)
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| Challenge: | Drug safety research is crucial for maintaining public health, but resources available to the public are limited. |
| Approach: | They propose an easy-to-use and interactive multi-source information visualisation platform for drug safety study. |
| Outcome: | The proposed platform provides a one-stop information analysis, retrieval, and annotation service. |
Extracting Event Temporal Relations via Hyperbolic Geometry (2021.emnlp-main)
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| Challenge: | Recent neural approaches to event temporal relation extraction map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. |
| Approach: | They propose to embed events into hyperbolic spaces to model hierarchical structures . they propose to use hyperbolical embeddings to directly infer event relations . |
| Outcome: | The proposed architecture is based on two approaches to encode events and their temporal relations in hyperbolic spaces. |
Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews (2021.eacl-main)
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| Challenge: | Existing models for sentiment-topic extraction assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’. |
| Approach: | They propose a Brand-Topic Model which aims to detect brand-associated polarity-bearing topics from product reviews. |
| Outcome: | The proposed model outperforms existing models on Amazon reviews and shows that it is more coherent and unique than existing models. |
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)
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| Challenge: | pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects. |
| Approach: | They investigate the ability of large language models (LLMs) to extract adverse events from medical text. |
| Outcome: | The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models. |
When in Doubt, Consult: Expert Debate for Sexism Detection via Confidence-Based Routing (2026.acl-long)
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| Challenge: | sexist content on social media is increasingly pervasive, often appearing in subtle, context-dependent forms that evade traditional classification methods. |
| Approach: | They propose a framework that unifies targeted training procedures to regularize supervision to scarce and noisy data with selective reasoning-based inference to handle ambiguous or borderline cases. |
| Outcome: | The proposed framework outperforms existing approaches across several public benchmarks . it bridges the gap between efficiency and reasoning with a dynamic routing mechanism . |
IntrEx: A Dataset for Modeling Engagement in Educational Conversations (2025.findings-emnlp)
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| Challenge: | IntrEx is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Approach: | They propose a large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
| Outcome: | The proposed dataset is the first large dataset annotated for interestingness and expected interestingness in teacher-student interactions. |
Position Bias Mitigation: A Knowledge-Aware Graph Model for Emotion Cause Extraction (2021.acl-long)
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| Challenge: | Existing models for ECE tend to explore relative position information and suffer from the dataset bias. |
| Approach: | They propose to generate adversarial examples where relative position is no longer indicative feature of cause clauses to address the dataset bias. |
| Outcome: | The proposed method performs on par with existing state-of-the-art methods on the original ECE dataset and is more robust against adversarial attacks compared to existing models. |
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (2022.naacl-main)
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| Challenge: | Existing approaches to detect vaccine attitudes on social media require abundant annotations and pre-defined aspect categories. |
| Approach: | They propose a semi-supervised approach to detect vaccine attitudes on social media . they use an autoencoding architecture to learn from unlabelled data the topical information of the domain . |
| Outcome: | The proposed model outperforms existing aspect-based models on stance detection and tweet clustering. |
Event Temporal Relation Extraction with Bayesian Translational Model (2023.eacl-main)
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| Challenge: | Existing methods to extract temporal relations between events lack a principled method to incorporate external knowledge. |
| Approach: | They propose a Bayesian-based method that models the temporal relation representations as latent variables and infers their values via Bayessian inference and translational functions. |
| Outcome: | The proposed method outperforms existing methods for event temporal relation extraction on three widely used datasets. |
Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation (2022.findings-emnlp)
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| Challenge: | Existing QA frameworks that use event-centric reasoning are lacking. |
| Approach: | They propose a novel QA model with contrastive learning and invertible event transformation . they use an invertable transformation matrix to project event vectors into a common event embedding space . |
| Outcome: | The proposed model achieves 8.4% gain in token-level F1 score and 3.0% gain in Exact Match score on the ESTER dataset. |
Neural Topic Model with Reinforcement Learning (D19-1)
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| Challenge: | Experimental results show superior performance on perplexity and topic coherence measures compared to state-of-the-art topic models. |
| Approach: | They propose to incorporate topic coherence measures as reward signals to guide the learning of a VAE-based topic model. |
| Outcome: | The proposed model is able to separating background words dynamically from topic words eliminating the pre-processing step of filtering infrequent and/or top frequent words, typically required for learning traditional topic models. |
Disentangling Aspect and Stance via a Siamese Autoencoder for Aspect Clustering of Vaccination Opinions (2023.findings-acl)
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| Challenge: | Existing approaches to mining public opinions about vaccines from social media make direct usage of supervision information constraining the models to predefined aspect classes while still not distinguishing those aspects from users’ stances. |
| Approach: | They propose a model for vaccination opinion mining from social media that disentangles users’ stances from opinions via a disentangling attention mechanism and a Swapping-Autoencoder. |
| Outcome: | The proposed model outperforms existing methods on aspect-based opinion mining. |
Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection (2025.acl-long)
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| Challenge: | Existing tools for sexism detection fail to capture subtle distinctions within sexist content, limiting their practical applicability. |
| Approach: | They propose two techniques to address class imbalance and nuanced nature of sexist language . definition-based data augmentation leverages category-specific definitions to generate semantically-aligned examples . |
| Outcome: | The proposed techniques improve accuracy across all tasks and improve reliability. |
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text (2022.emnlp-main)
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Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron Wallace, Bino John, Nigel Greene, Joseph Kim, Yulan He
| Challenge: | Using NLP methods to discover and extract adverse drug events from unstructured textual data is difficult because it requires time-consuming manual curation. |
| Approach: | They propose to use a hierarchical event schema to extract annotated events from medical case reports and biomedical literature to analyze patient data. |
| Outcome: | The proposed dataset is the largest public dataset to date and contains over 5000 events from medical case reports and biomedical literature. |
Cascading Large Language Models for Salient Event Graph Generation (2025.naacl-long)
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| Challenge: | Existing studies on event graph generation rely on distant supervision for event graphs . |
| Approach: | They propose a CAscading Large Language Model framework for SAlient Event graph generation which leverages the capabilities of LLMs and eliminates the need for costly human annotations. |
| Outcome: | The proposed method outperforms baseline models on a human-annotated test set. |
Set-Aligning Framework for Auto-Regressive Event Temporal Graph Generation (2024.naacl-long)
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| Challenge: | Existing methods for constructing event temporal graphs have been suboptimal . authors propose a set-aligning framework for the effective utilisation of Large Language Models . |
| Approach: | They propose a set-aligning framework for the effective utilisation of Large Language Models to alleviate text generation loss penalties. |
| Outcome: | The proposed framework surpasses existing baselines for event temporal graph generation. |
Boosting Low-Resource Biomedical QA via Entity-Aware Masking Strategies (2021.eacl-main)
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| Challenge: | Biomedical question-answering (QA) provides users with high-quality information from a vast scientific literature. |
| Approach: | They propose to use a biomedical entity-aware masking strategy to fine-tune masked language models to their domains. |
| Outcome: | The proposed approach is an adaptation process for masked LMs, not memory or components. |
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews (2021.naacl-main)
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| Challenge: | Existing topic models may extract topics associated with writers’ subjective opinions mixed with those related to factual descriptions. |
| Approach: | They propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. |
| Outcome: | The proposed model shows improved coherence and variety of topics, consistent disentanglement rate, and superior sentiment classification performance to other supervised topic models. |
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering (2020.coling-main)
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| Challenge: | Existing QA systems deal with factoid questions and assume a simplified setup such as multiple-choice questions, retrieving spans of text from given documents, and filling in the blanks. |
| Approach: | They propose a cross-passage hierarchical memory network for question answering via text generation that extends XLNet by adding an auxiliary memory module to the context memory and answer memory. |
| Outcome: | The proposed architecture outperforms the state-of-the-art generative QA framework with better syntactically well-formed answers and increased precision on the AmazonQA review dataset. |