This work investigates the acquisition of formal linguistic competence by neural language models, hypothesizing that languages with complex grammar, such as Basque, present substantial challenges during the pre-training phase. Basque is distinguished by its complex morphology and flexible word order, potentially complicating grammar extraction. In our analysis, we evaluated the grammatical knowledge of BERT models trained under various pre-training configurations, considering factors such as corpus size, model size, number of epochs, and the use of lemmatization. To assess this grammatical knowledge, we constructed the BL2MP (Basque L2 student-based Minimal Pairs) test set. This test set consists of minimal pairs, each containing both a grammatically correct and an incorrect sentence, sourced from essays authored by students at different proficiency levels in the Basque language. Additionally, our analysis explores the difficulties in learning various grammatical phenomena, the challenges posed by flexible word order, and the influence of the student’s proficiency level on the difficulty of correcting grammar errors.

XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper, we expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. The new dataset, dubbed XNLIeu, has been developed by first machine-translating the English XNLI corpus into Basque, followed by a manual post-edition step. We have conducted a series of experiments using mono- and multilingual LLMs to assess a) the effect of professional post-edition on the MT system; b) the best cross-lingual strategy for NLI in Basque; and c) whether the choice of the best cross-lingual strategy is influenced by the fact that the dataset is built by translation. The results show that post-edition is necessary and that the translate-train cross-lingual strategy obtains better results overall, although the gain is lower when tested in a dataset that has been built natively from scratch. Our code and datasets are publicly available under open licenses.

In the news, statements from information sources are often quoted, made by individuals who interact in the news. Detecting those quotes and the gender of their sources is a key task when it comes to media analysis from a gender perspective. It is a challenging task: the structure of the quotes is variable, gender marks are not present in many languages, and quote authors are often omitted due to frequent use of coreferences. This paper proposes a strategy to measure the presence of women and men as information sources in news. We approach the problem of detecting sentences including quotes and the gender of the speaker as a joint task, by means of a supervised multiclass classifier of sentences. We have created the first datasets for Spanish and Basque by manually annotating quotes and the gender of the associated sources in news items. The results obtained show that BERT based approaches are significantly better than bag-of-words based classical ones, achieving accuracies close to 90%. We also analyse a bilingual learning strategy and generating additional training examples synthetically; both provide improvements up to 3.4% and 5.6%, respectively.