Online sexism increasingly appears in subtle, context-dependent forms that evade traditional detection methods.Its interpretation often depends on overlapping linguistic, psychological, legal, and cultural dimensions, which produce noisy and sometimes contradictory signals in annotated datasets.These inconsistencies, combined with label scarcity and class imbalance, result in unstable decision boundaries and cause fine-tuned models to overlook subtler, underrepresented forms of harm.To address these challenges, we propose a twostage framework that unifies (i) targeted training procedures to better regularize supervision to scarce and noisy data with (ii) selective, reasoning-based inference to handle ambiguous or borderline cases.First, we stabilize the training combining class-balanced focal loss, class-aware batching, and post-hoc threshold calibration, strategies for the first time adapted for this domain to mitigate label imbalance and noisy supervision.Second, we bridge the gap between efficiency and reasoning with a a dynamic routing mechanism that distinguishes between unambiguous instances and complex cases requiring a deliberative process.This reasoning process results in the novel Collaborative Expert Judgment (CEJ) module which prompts multiple personas and consolidates their reasoning through a judge model.Our approach outperforms existing approaches across several public benchmarks, with F1 gains of +4.48% and +1.30% on EDOS Tasks A and B, respectively, and a +2.79% improvement in ICM on EXIST 2025 Task 1.1.

Predicting unknown drug-drug interactions (DDIs) is crucial for improving medication safety. Previous efforts in DDI prediction have typically focused on binary classification or predicting DDI categories, with the absence of explanatory insights that could enhance trust in these predictions. In this work, we propose to generate natural language explanations for DDI predictions, enabling the model to reveal the underlying pharmacodynamics and pharmacokinetics mechanisms simultaneously as making the prediction. To do this, we have collected DDI explanations from DDInter and DrugBank and developed various models for extensive experiments and analysis. Our models can provide accurate explanations for unknown DDIs between known drugs. This paper contributes new tools to the field of DDI prediction and lays a solid foundation for further research on generating explanations for DDI predictions.

The detection of sexism in online content remains an open problem, as harmful language disproportionately affects women and marginalized groups.While automated systems for sexism detection have been developed, they still face two key challenges: data sparsity and the nuanced nature of sexist language.Even in large, well-curated datasets like the Explainable Detection of Online Sexism (EDOS), severe class imbalance hinders model generalization.Additionally, the overlapping and ambiguous boundaries of fine-grained categories introduce substantial annotator disagreement, reflecting the difficulty of interpreting nuanced expressions of sexism.To address these challenges, we propose two prompt-based data augmentation techniques: Definition-based Data Augmentation (DDA), which leverages categoryspecific definitions to generate semanticallyaligned synthetic examples, and Contextual Semantic Expansion (CSE), which targets systematic model errors by enriching examples with task-specific semantic features.To further improve reliability in fine-grained classification, we introduce an ensemble strategy that resolves prediction ties by aggregating complementary perspectives from multiple language models.Our experimental evaluation on the EDOS dataset demonstrates state-of-the-art performance across all tasks, with notable improvements of macro F1 by 1.5 points for binary classification (Task A) and 4.1 points for fine-grained classification (Task C) 1 .Warning: This paper includes examples that might be offensive and upsetting.1 Code and resources publicly available at: https://github.com/Sahrish42/explaining_matters_ sexism_detection_acl2025 Variation 1: "They lack the mental toughness to handle high-stakes leadership roles" Variation 2: "You can't trust women with important decisions?they're too caught up in their emotions."Variation 3: "Women just let their feelings get in the way of making logical choices." VariationsAs part of a fine-grained sexism detection project aimed at categorizing harmful language into distinct sexist categories: Ø Generate 3 Variations of the following Example Ø Reflect the Sexist Intent: Ensure each variation clearly embodies the sexist intent and nature described in the vector and definition.Ø Match the Tone: The tone should align with the emotional intensity and aggression described in definition.Ø Mimic Informal Social Media Language: Craft statements that resemble informal language typical of social media platforms like Reddit or Gab, using slang, abbreviations, or casual expressions as appropriate.• Vector: 2.1 Descriptive Attacks• Definition: Characterizing or describing women in a derogatory manner.This could include negative generalizations about women's abilities, appearance, sexual behavior, intellect, character, or morals.• Example: "Women are too soft and emotional to make rational decisions''

Xingwei Tan, Yuxiang Zhou, Gabriele Pergola, Yulan He. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Xingwei Tan, Yuxiang Zhou, Gabriele Pergola, Yulan He. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Existing models to extract temporal relations between events lack a principled method to incorporate external knowledge.In this study, we introduce Bayesian-Trans, a Bayesian learningbased method that models the temporal relation representations as latent variables and infers their values via Bayesian inference and translational functions.Compared to conventional neural approaches, instead of performing point estimation to find the best set parameters, the proposed model infers the parameters' posterior distribution directly, enhancing the model's capability to encode and express uncertainty about the predictions.Experimental results on the three widely used datasets show that Bayesian-Trans outperforms existing approaches for event temporal relation extraction.We additionally present detailed analyses on uncertainty quantification, comparison of priors, and ablation studies, illustrating the benefits of the proposed approach.1

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health.Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health professionals, physicians, and pharmacists, and information voluntarily submitted by patients.In this scenario, facilitating analysis of such reports via automation has the potential to rapidly identify safety signals.Unfortunately, public resources for developing natural language models for this task are scant.We present PHEE, a novel dataset for pharmacovigilance comprising over 5000 annotated events from medical case reports and biomedical literature, making it the largest such public dataset to date.We describe the hierarchical event schema designed to provide coarse and fine-grained information about patients' demographics, treatments and (side) effects.Along with the discussion of the dataset, we present a thorough experimental evaluation of current state-of-the-art approaches for biomedical event extraction, point out their limitations, and highlight open challenges to foster future research in this area 1 .

Lixing Zhu, Zheng Fang, Gabriele Pergola, Robert Procter, Yulan He. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Hanqi Yan, Lin Gui, Gabriele Pergola, Yulan He. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Lixing Zhu, Gabriele Pergola, Lin Gui, Deyu Zhou, Yulan He. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Biomedical question-answering (QA) has gained increased attention for its capability to provide users with high-quality information from a vast scientific literature.Although an increasing number of biomedical QA datasets has been recently made available, those resources are still rather limited and expensive to produce.Transfer learning via pre-trained language models (LMs) has been shown as a promising approach to leverage existing general-purpose knowledge.However, finetuning these large models can be costly and time consuming, often yielding limited benefits when adapting to specific themes of specialised domains, such as the COVID-19 literature.To bootstrap further their domain adaptation, we propose a simple yet unexplored approach, which we call biomedical entity-aware masking (BEM).We encourage masked language models to learn entity-centric knowledge based on the pivotal entities characterizing the domain at hand, and employ those entities to drive the LM fine-tuning.The resulting strategy is a downstream process applicable to a wide variety of masked LMs, not requiring additional memory or components in the neural architectures.Experimental results show performance on par with state-of-the-art models on several biomedical QA datasets.

In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brandassociated polarity-bearing topics from product reviews.Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as 'positive', 'negative' and 'neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., 'shaver' or 'cream') while its associated sentiment gradually varies from negative to positive.BTM is built on the Poisson factorisation model with the incorporation of adversarial learning.It has been evaluated on a dataset constructed from Amazon reviews.Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polaritybearing topics.

Detecting events and their evolution through time is a crucial task in natural language understanding.Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs.However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations.We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures.We introduce two approaches to encode events and their temporal relations in hyperbolic spaces.One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations.In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task.Thorough experimental assessments on widely used datasets have shown the benefits of revisiting the tasks on a different geometrical space, resulting in state-of-the-art performance on several standard metrics.Finally, the ablation study and several qualitative analyses highlighted the rich event semantics implicitly encoded into hyperbolic spaces. 1

The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTM).Although these approaches have achieved significant results, surprisingly very little work has been done on how to disentangle the latent topics.Existing topic models when applied to reviews may extract topics associated with writers' subjective opinions mixed with those related to factual descriptions such as plot summaries in movie and book reviews.It is thus desirable to automatically separate opinion topics from plot/neutral ones enabling a better interpretability.In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones.We conduct an extensive experimental assessment introducing a new collection of movie and book reviews paired with their plots, namely MOBO dataset, showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation.It extends XLNet (Yang et al., 2019) introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidence, and the answer memory working as a buffer continually refining the generated answers.Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset.An additional qualitative analysis revealed the interpretability introduced by the memory module.

Lin Gui, Jia Leng, Gabriele Pergola, Yu Zhou, Ruifeng Xu, Yulan He. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing techniques.