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 Large-scale sense-annotated corpora are important for a range of tasks but are hard to come by. Dictionaries that record and describe the vocabulary of a language often offer a small set of real-world example sentences for each sense of a word. However, on their own, these sentences are too few to be used as diachronic sense-annotated corpora. We propose a targeted strategy for training and evaluating generative models producing historically and semantically accurate word usages given any word, sense definition, and year triple. Our results demonstrate that fine-tuned models can generate usages with the same properties as real-world example sentences from a reference dictionary. Thus the generated usages will be suitable for training and testing computational models where large-scale sense-annotated corpora are needed but currently unavailable.

There is abundant evidence of the fact that the way words change their meaning can be classified in different types of change, highlighting the relationship between the old and new meanings (among which generalization, specialization and co-hyponymy transfer).In this paper, we present a way of detecting these types of change by constructing a model that leverages information both from synchronic lexical relations and definitions of word meanings.Specifically, we use synset definitions and hierarchy information from WordNet and test it on a digitized version of Blank's (1997) dataset of semantic change types.Finally, we show how the sense relationships can improve models for both approximation of human judgments of semantic relatedness as well as binary Lexical Semantic Change Detection.

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.

Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte Im Walde, Nina Tahmasebi. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

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.

Aleksandrs Berdicevskis, Gerlof Bouma, Robin Kurtz, Felix Morger, Joey Öhman, Yvonne Adesam, Lars Borin, Dana Dannélls, Markus Forsberg, Tim Isbister, Anna Lindahl, Martin Malmsten, Faton Rekathati, Magnus Sahlgren, Elena Volodina, Love Börjeson, Simon Hengchen, Nina Tahmasebi. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Word meaning is notoriously difficult to capture, both synchronically and diachronically.In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments.We describe in detail the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible -diachronic and synchronic -uses for this dataset.

State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment.We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise.We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset.We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.

Human language constantly evolves due to the changing world and the need for easier forms of expression and communication. Our knowledge of language evolution is however still fragmentary despite significant interest of both researchers as well as wider public in the evolution of language. In this paper, we present an interactive framework that permits users study the evolution of words and concepts. The system we propose offers a rich online interface allowing arbitrary queries and complex analytics over large scale historical textual data, letting users investigate changes in meaning, context and word relationships across time.