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.

This paper explores the use of existing stateof-the-art speech recognition models (ASR) for the task of transcribing speech with narrow phonetic transcriptions using the International Phonetic Alphabet (Speech-to-IPA, STIPA).Unlike conventional ASR systems focused on orthographic output for high-resource languages, STIPA can be used as a language-agnostic interface valuable for documenting under-resourced and unwritten languages.We introduce a new STIPA dataset for South Levantine Arabic and present a large-scale evaluation of STIPA models across 21 language families.Additionally, we provide a use case on Sanna, a severely endangered language.Our findings show that fine-tuned ASR models can produce accurate IPA transcriptions with limited supervision, significantly reducing phonetic error rates even in extremely low-resource settings.The results highlight the potential of STIPA for scalable language documentation and the relevance of training data composition.

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.

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.