Laura García-Sardiña

Also published as: Laura García-sardiña


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BaSCo: An Annotated Basque-Spanish Code-Switching Corpus for Natural Language Understanding
Maia Aguirre | Laura García-Sardiña | Manex Serras | Ariane Méndez | Jacobo López
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The main objective of this work is the elaboration and public release of BaSCo, the first corpus with annotated linguistic resources encompassing Basque-Spanish code-switching. The mixture of Basque and Spanish languages within the same utterance is popularly referred to as Euskañol, a widespread phenomenon among bilingual speakers in the Basque Country. Thus, this corpus has been created to meet the demand of annotated linguistic resources in Euskañol in research areas such as multilingual dialogue systems. The presented resource is the result of translating to Euskañol a compilation of texts in Basque and Spanish that were used for training the Natural Language Understanding (NLU) models of several task-oriented bilingual chatbots. Those chatbots were meant to answer specific questions associated with the administration, fiscal, and transport domains. In addition, they had the transverse potential to answer to greetings, requests for help, and chit-chat questions asked to chatbots. BaSCo is a compendium of 1377 tagged utterances with every sample annotated at three levels: (i) NLU semantic labels, considering intents and entities, (ii) code-switching proportion, and (iii) domain of origin.

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Exploiting In-Domain Bilingual Corpora for Zero-Shot Transfer Learning in NLU of Intra-Sentential Code-Switching Chatbot Interactions
Maia Aguirre | Manex Serras | Laura García-sardiña | Jacobo López-fernández | Ariane Méndez | Arantza Del Pozo
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

Code-switching (CS) is a very common phenomenon in regions with various co-existing languages. Since CS is such a frequent habit in informal communications, both spoken and written, it also arises naturally in Human-Machine Interactions. Therefore, in order for natural language understanding (NLU) not to be degraded, CS must be taken into account when developing chatbots. The co-existence of multiple languages in a single NLU model has become feasible with multilingual language representation models such as mBERT. In this paper, the efficacy of zero-shot cross-lingual transfer learning with mBERT for NLU is evaluated on a Basque-Spanish CS chatbot corpus, comparing the performance of NLU models trained using in-domain chatbot utterances in Basque and/or Spanish without CS. The results obtained indicate that training joint multi-intent classification and entity recognition models on both languages simultaneously achieves best performance, better capturing the CS patterns.


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HitzalMed: Anonymisation of Clinical Text in Spanish
Salvador Lima Lopez | Naiara Perez | Laura García-Sardiña | Montse Cuadros
Proceedings of the Twelfth Language Resources and Evaluation Conference

HitzalMed is a web-framed tool that performs automatic detection of sensitive information in clinical texts using machine learning algorithms reported to be competitive for the task. Moreover, once sensitive information is detected, different anonymisation techniques are implemented that are configurable by the user –for instance, substitution, where sensitive items are replaced by same category text in an effort to generate a new document that looks as natural as the original one. The tool is able to get data from different document formats and outputs downloadable anonymised data. This paper presents the anonymisation and substitution technology and the demonstrator which is publicly available at


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ES-Port: a Spontaneous Spoken Human-Human Technical Support Corpus for Dialogue Research in Spanish
Laura García-Sardiña | Manex Serras | Arantza del Pozo
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)