Cross-lingual transfer-learning is widely used in Event Extraction for low-resource languages and involves a Multilingual Language Model that is trained in a source language and applied to the target language. This paper studies whether the typological similarity between source and target languages impacts the performance of cross-lingual transfer, an under-explored topic. We first focus on Basque as the target language, which is an ideal target language because it is typologically different from surrounding languages. Our experiments on three Event Extraction tasks show that the shared linguistic characteristic between source and target languages does have an impact on transfer quality. Further analysis of 72 language pairs reveals that for tasks that involve token classification such as entity and event trigger identification, common writing script and morphological features produce higher quality cross-lingual transfer. In contrast, for tasks involving structural prediction like argument extraction, common word order is the most relevant feature. In addition, we show that when increasing the training size, not all the languages scale in the same way in the cross-lingual setting. To perform the experiments we introduce EusIE, an event extraction dataset for Basque, which follows the Multilingual Event Extraction dataset (MEE). The dataset and code are publicly available.

This paper analyses the challenge of working with dialectal variation when semi-automatically normalising and analysing historical Basque texts. This work is part of a more general ongoing project for the construction of a morphosyntactically annotated historical corpus of Basque called Basque in the Making (BIM): A Historical Look at a European Language Isolate, whose main objective is the systematic and diachronic study of a number of grammatical features. This will be not only the first tagged corpus of historical Basque, but also a means to improve language processing tools by analysing historical Basque varieties more or less distant from present-day standard Basque.

This paper presents a Basque corpus where Verbal Multiword Expressions (VMWEs) were annotated following universal guidelines. Information on the annotation is given, and some ideas for discussion upon the guidelines are also proposed. The corpus is useful not only for NLP-related research, but also to draw conclusions on Basque phraseology in comparison with other languages.

This paper presents the work that has been carried out to annotate semantic roles in the Basque Dependency Treebank (BDT). We will describe the resources we have used and the way the annotation of 100 verbs has been done. We decide to follow the model proposed in the PropBank project that has been deployed in other languages, such as Chinese, Spanish, Catalan and Russian. The resources used are: an in-house database with syntactic/semantic subcategorization frames for Basque verbs, an English-Basque verb mapping based on Levin’s classification and the BDT itself. Detailed guidelines for human taggers have been established as a result of this annotation process. In addition, we have characterized the information associated to the semantic tag. Besides, and based on this study, we will define semi-automatic procedures that will facilitate the task of manual annotation for the rest of the verbs of the Treebank. We have also adapted AbarHitz, a tool used in the construction of the BDT, for the task of annotating semantic roles according to the proposed characterization.