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Found 15 papers, 1 papers with code
Findings of the WMT 2021 Biomedical Translation Shared Task: Summaries of Animal Experiments as New Test Set
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.
Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario
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.
Findings of the WMT 2020 Biomedical Translation Shared Task: Basque, Italian and Russian as New Additional Languages
Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities.
Ixamed’s submission description for WMT20 Biomedical shared task: benefits and limitations of using terminologies for domain adaptation
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.
Approximate Nearest Neighbour Extraction Techniques and Neural Networks for Suicide Risk Prediction in the CLPsych 2022 Shared Task
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.
MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering
So far the benchmark is available in four languages, but we hope that this work may encourage further development to other languages.
Explanatory Argument Extraction of Correct Answers in Resident Medical Exams
Comprehensive experimentation with language models for Spanish shows that sometimes multilingual models fare better than monolingual ones, even outperforming models which have been adapted to the medical domain.
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital Medicine
Providing high quality explanations for AI predictions based on machine learning is a challenging and complex task.
IxaMed at PharmacoNER Challenge 2019
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 impact of simple feature engineering in multilingual medical NER
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.
