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.

Cross-lingual transfer allows models to perform tasks in languages unseen during training and is often assumed to benefit from increased multilinguality. In this work, we challenge this assumption in the context of two underexplored, sense-aware tasks: polysemy disambiguation and lexical semantic change. Through a large-scale analysis across 28 languages, we show that multilingual training is neither necessary nor inherently beneficial for effective transfer. Instead, we find that confounding factors, such as fine-tuning data composition and evaluation artifacts, can better account for the perceived advantages of multilinguality. Our findings call for more rigorous evaluations in multilingual NLP, and more nuanced and sensible choice of models for transfer. We release fine-tuned models and benchmarks to support further research, with implications extending to low-resource and typologically diverse languages.

This paper addresses the critical need for high-quality evaluation datasets in low-resource languages to advance cross-lingual transfer. While cross-lingual transfer offers a key strategy for leveraging multilingual pretraining to expand language technologies to understudied and typologically diverse languages, its effectiveness is dependent on quality and suitable benchmarks. We release new sense-annotated datasets of sentences containing polysemous words, spanning nine low-resource languages across diverse language families and scripts. To facilitate dataset creation, the paper presents a demonstrably beneficial semi-automatic annotation method. The utility of the datasets is demonstrated through Word-in-Context (WiC) formatted experiments that evaluate transfer on these low-resource languages. Results highlight the importance of targeted dataset creation and evaluation for effective polysemy disambiguation in low-resource settings and transfer studies. The released datasets and code aim to support further research into fair, robust, and truly multilingual NLP.

This student paper explores the potential of augmenting computational models of semantic change with sentiment information. It tests the efficacy of this approach on the English SemEval of Lexical Semantic Change and its associated historical corpora. We first establish the feasibility of our approach by demonstrating that existing models extract reliable sentiment information from historical corpora, and then validate that words that underwent semantic change also show greater sentiment change in comparison to historically stable words. We then integrate sentiment information into standard models of semantic change for individual words, and test if this can improve the overall performance of the latter, showing mixed results. This research contributes to our understanding of language change by providing the first attempt to enrich standard models of semantic change with additional information. It taps into the multifaceted nature of language change, that should not be reduced only to binary or scalar report of change, but adds additional dimensions to this change, sentiment being only one of these. As such, this student paper suggests novel directions for future work in integrating additional, more nuanced information of change and interpretation for finer-grained semantic change analysis.