Retrieval of previously fact-checked claims is a well-established task, whose automation can assist professional fact-checkers in the initial steps of information verification.Previous works have mostly tackled the task monolingually, i.e., having both the input and the retrieved claims in the same language.However, especially for languages with a limited availability of fact-checks and in case of global narratives, such as pandemics, wars, or international politics, it is crucial to be able to retrieve claims across languages.In this work, we examine strategies to improve the multilingual and crosslingual performance, namely selection of negative examples (in the supervised) and re-ranking (in the unsupervised setting).We evaluate all approaches on a dataset containing posts and claims in 47 languages (283 language combinations).We observe that the best results are obtained by using LLMbased re-ranking, followed by fine-tuning with negative examples sampled using a sentence similarity-based strategy.Most importantly, we show that crosslinguality is a setup with its own unique characteristics compared to the multilingual setup. 1 * These authors contributed equally to this work.

Converging societal and technical factors have transformed language technologies into userfacing applications used by the general public across languages.Machine Translation (MT) has become a global tool, with cross-lingual services now also supported by dialogue systems powered by multilingual Large Language Models (LLMs).Widespread accessibility has extended MT's reach to a vast base of lay users, many with little to no expertise in the languages or the technology itself.And yet, the understanding of MT consumed by such a diverse group of users-their needs, experiences, and interactions with multilingual systemsremains limited.In our position paper, we first trace the evolution of MT user profiles, focusing on non-experts and how their engagement with technology may shift with the rise of LLMs.Building on an interdisciplinary body of work, we identify three factors-usability, trust, and literacy-that are central to shaping user interactions and must be addressed to align MT with user needs.By examining these dimensions, we provide insights to guide the progress of more user-centered MT.

Alan Ramponi, Agnese Daffara, Sara Tonelli. 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.

The use of synthetic data for training models for a variety of NLP tasks is now widespread.However, previous work reports mixed results with regards to its effectiveness on highly subjective tasks such as hate speech detection.In this paper, we present an in-depth qualitative analysis of the potential and specific pitfalls of synthetic data for hate speech detection in English, with 3,500 manually annotated examples.We show that, across different models, synthetic data created through paraphrasing gold texts can improve out-of-distribution robustness from a computational standpoint.However, this comes at a cost: synthetic data fails to reliably reflect the characteristics of real-world data on a number of linguistic dimensions, it results in drastically different class distributions, and it heavily reduces the representation of both specific identity groups and intersectional hate.Warning: this paper contains examples that may be offensive or upsetting.

Abstract Italy is characterized by a one-of-a-kind linguistic diversity landscape in Europe, which implicitly encodes local knowledge, cultural traditions, artistic expressions, and history of its speakers. However, most local languages and dialects in Italy are at risk of disappearing within a few generations. The NLP community has recently begun to engage with endangered languages, including those of Italy. Yet, most efforts assume that these varieties are under-resourced language monoliths with an established written form and homogeneous functions and needs, and thus highly interchangeable with each other and with high-resource, standardized languages. In this paper, we introduce the linguistic context of Italy and challenge the default machine-centric assumptions of NLP for Italy’s language varieties. We advocate for a shift in the paradigm from machine-centric to speaker-centric NLP, and provide recommendations and opportunities for work that prioritizes languages and their speakers over technological advances. To facilitate the process, we finally propose building a local community towards responsible, participatory efforts aimed at supporting vitality of languages and dialects of Italy.

Warning: this paper contains content that may be offensive or upsetting.Avoiding to rely on dataset artifacts to predict hate speech is at the cornerstone of robust and fair hate speech detection.In this paper we critically analyze lexical biases in hate speech detection via a cross-platform study, disentangling various types of spurious and authentic artifacts and analyzing their impact on out-of-distribution fairness and robustness.We experiment with existing approaches and propose simple yet surprisingly effective datacentric baselines.Our results on English data across four platforms show that distinct spurious artifacts require different treatments to ultimately attain both robustness and fairness in hate speech detection.To encourage research in this direction, we release all baseline models and the code to compute artifacts, pointing it out as a complementary and necessary addition to the data statements practice.1

Rob van der Goot, Ibrahim Sharaf, Aizhan Imankulova, Ahmet Üstün, Marija Stepanović, Alan Ramponi, Siti Oryza Khairunnisa, Mamoru Komachi, Barbara Plank. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

Deep neural networks excel at learning from labeled data and achieve state-of-the-art results on a wide array of Natural Language Processing tasks.In contrast, learning from unlabeled data, especially under domain shift, remains a challenge.Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data.This is a more challenging yet a more widely applicable setup.We outline methods, from early traditional non-neural methods to pre-trained model transfer.We also revisit the notion of domain, and we uncover a bias in the type of Natural Language Processing tasks which received most attention.Lastly, we outline future directions, particularly the broader need for out-of-distribution generalization of future NLP. 1

We introduce Biomedical Event Extraction as Sequence Labeling (BEESL), a joint endto-end neural information extraction model.BEESL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning.BEESL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools.BEESL outperforms the current best system (Li et al., 2019) on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1, establishing a new state of the art for the task.Importantly, we also provide first results on biomedical event extraction without gold entity information.Empirical results show that BEESL's speed and accuracy makes it a viable approach for large-scale real-world scenarios.1

Lexical normalization is the task of translating non-standard social media data to a standard form. Previous work has shown that this is beneficial for many downstream tasks in multiple languages. However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data. In this paper, we discuss the creation of a lexical normalization dataset for Italian. After two rounds of annotation, a Cohen’s kappa score of 78.64 is obtained. During this process, we also analyze the inter-annotator agreement for this task, which is only rarely done on datasets for lexical normalization,and when it is reported, the analysis usually remains shallow. Furthermore, we utilize this dataset to train a lexical normalization model and show that it can be used to improve dependency parsing of social media data. All annotated data and the code to reproduce the results are available at: http://bitbucket.org/robvanderg/normit.

Biomedical event extraction is a crucial task in order to automatically extract information from the increasingly growing body of biomedical literature. Despite advances in the methods in recent years, most event extraction systems are still evaluated in-domain and on complete event structures only. This makes it hard to determine the performance of intermediate stages of the task, such as edge detection, across different corpora. Motivated by these limitations, we present the first cross-domain study of edge detection for biomedical event extraction. We analyze differences between five existing gold standard corpora, create a standardized benchmark corpus, and provide a strong baseline model for edge detection. Experiments show a large drop in performance when the baseline is applied on out-of-domain data, confirming the need for domain adaptation methods for the task. To encourage research efforts in this direction, we make both the data and the baseline available to the research community: https://www.cosbi.eu/cfx/9985.