1 code implementation • • Rachel Bawden, Giorgio Maria Di Nunzio, Cristian Grozea, Inigo Jauregi Unanue, Antonio Jimeno Yepes, Nancy Mah, David Martinez, Aurélie Névéol, Mariana Neves, Maite Oronoz, Olatz Perez-de-Viñaspre, Massimo Piccardi, Roland Roller, Amy Siu, Philippe Thomas, Federica Vezzani, Maika Vicente Navarro, Dina Wiemann, Lana Yeganova
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities.
Regarding the techniques used, we base on the findings from our previous works for translating clinical texts into Basque, making use of clinical terminology for adapting the MT systems to the clinical domain.
no code implementations • • Lana Yeganova, Dina Wiemann, Mariana Neves, Federica Vezzani, Amy Siu, Inigo Jauregi Unanue, Maite Oronoz, Nancy Mah, Aurélie Névéol, David Martinez, Rachel Bawden, Giorgio Maria Di Nunzio, Roland Roller, Philippe Thomas, Cristian Grozea, Olatz Perez-de-Viñaspre, Maika Vicente Navarro, Antonio Jimeno Yepes
In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque.
This paper describes the participation of our group on the CLPsych 2022 shared task. For task A, which tries to capture changes in mood over time, we have applied an Approximate Nearest Neighbour (ANN) extraction technique with the aim of relabelling the user messages according to their proximity, based on the representation of these messages in a vector space.
Recently, diverse approaches have been proposed to get better automatic evaluation results of NMT models using back-translation, including the use of sampling instead of beam search as decoding algorithm for creating the synthetic corpus.
no code implementations • 9 Jun 2023 • Rodrigo Agerri, Iñigo Alonso, Aitziber Atutxa, Ander Berrondo, Ainara Estarrona, Iker Garcia-Ferrero, Iakes Goenaga, Koldo Gojenola, Maite Oronoz, Igor Perez-Tejedor, German Rigau, Anar Yeginbergenova
Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task.
In order to identify named entities we have made use of a Bi-LSTM with a CRF on top in combination with different types of word embeddings.
The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER.