Pierluigi Cassotti, Naomi Baes, Stefano De Pascale, Jáder Martins Camboim de Sá, Francesco Periti, Nick Haslam, Dirk Geeraerts, Nina Tahmasebi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The task of Definition Generation has recently gained attention as an interpretable approach to modeling word meaning.Thus far, most research has been conducted in English, with limited work and resources for other languages.In this work, we expand Definition Generation beyond English to a suite of 22 languages and evaluate Llama-based models within a monolingual, multilingual, and cross-lingual setting.Our experiments show that monolingual fine-tuning consistently outperforms pretrained baselines, with the largest gains observed in languages with lower initial performance; and that multilingual fine-tuning does not consistently improve performance on the individual fine-tuning languages.Our cross-lingual evaluation reveals that models fine-tuned on a single language typically lose the ability to generate definitions in other languages, whereas multilingual models exhibit robust generalization even to languages unseen during fine-tuning.

Abstract Modern data mining applications require to perform incremental clustering over dynamic datasets by tracing temporal changes over the resulting clusters. In this paper, we propose A-Posteriori affinity Propagation (APP), an incremental extension of affinity propagation (AP) based on cluster consolidation and cluster stratification to achieve faithfulness and forgetfulness. APP enforces incremental clustering where i) new arriving objects are dynamically consolidated into previous clusters without the need to re-execute clustering over the entire dataset of objects, and ii) a faithful sequence of clustering results is produced and maintained over time, while allowing to forget obsolete clusters with decremental learning functionalities. Four popular labeled datasets are used to test the performance of APP with respect to benchmark clustering performances obtained by conventional AP and incremental affinity propagation based on nearest neighbor assignment algorithms. Experimental results show that APP achieves comparable clustering performance while enforcing scalability at the same time.

Modern language models are capable of contextualizing words based on their surrounding context.However, this capability is often compromised due to semantic change that leads to words being used in new, unexpected contexts not encountered during pre-training.In this paper, we model semantic change by studying the effect of unexpected contexts introduced by lexical replacements.We propose a replacement schema where a target word is substituted with lexical replacements of varying relatedness, thus simulating different kinds of semantic change.Furthermore, we leverage the replacement schema as a basis for a novel interpretable model for semantic change.We are also the first to evaluate the use of LLaMa for semantic change detection.

Modeling lexical semantics is a challenging task, often suffering from interpretability pitfalls.In this paper, we delve into the generation of dictionary-like sense definitions and explore their utility for modeling word meaning.We fine-tuned two Llama models and include an existing T5-based model in our evaluation.Firstly, we evaluate the quality of the generated definitions on existing English benchmarks, setting new state-of-the-art results for the Definition Generation task.Next, we explore the use of definitions generated by our models as intermediate representations subsequently encoded as sentence embeddings.We evaluate this approach on lexical semantics tasks such as the Word-in-Context, Word Sense Induction, and Lexical Semantic Change, setting new state-ofthe-art results in all three tasks when compared to unsupervised baselines.

Current approaches for detecting text reuse do not focus on recontextualization, i.e., how the new context(s) of a reused text differs from its original context(s).In this paper, we propose a novel framework called TRoTR that relies on the notion of topic relatedness for evaluating the diachronic change of context in which text is reused.TRoTR includes two NLP tasks: TRiC and TRaC.TRiC is designed to evaluate the topic relatedness between a pair of recontextualizations. TRaC is designed to evaluate the overall topic variation within a set of recontextualizations.We also provide a curated TRoTR benchmark of biblical text reuse, human-annotated with topic relatedness.The benchmark exhibits an inter-annotator agreement of .811.We evaluate multiple, established SBERT models on the TRoTR tasks and find that they exhibit greater sensitivity to textual similarity than topic relatedness.Our experiments show that fine-tuning these models can mitigate such a kind of sensitivity.

Francesco Periti, Nina Tahmasebi. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.