Extraction d’arguments basée sur les transformateurs pour des applications dans le domaine de la santé (Transformer-based Argument Mining for Healthcare Applications) (2021)

Abstract: Nous présentons des résumés en français et en anglais de l’article (Mayer et al., 2020) présenté à la conférence 24th European Conference on Artificial Intelligence (ECAI-2020) en 2020.

## english abstract argument(ation) mining (am) typically aims at identifying argumentative components in text and predicting the relations among them. evidence-based decision making @xcite @xcite in the healthcare domain targets at supporting clinicians in their deliberation process to establish the best course of action for the case under evaluation. however, despite its natural employment in healthcare applications, only few approaches have applied am methods to this kind of text @xcite @xcite , and their contribution is limited to the detection of argument components, disregarding the more complex phase of predicting the relations among them. in addition, no huge annotated dataset for am is available for the healthcare domain. in this paper @xcite , we covered this gap, and we answered the following research question : how to define a complete am pipeline for clinical trials ? to answer this question, we propose a deep bidirectional transformer approach combined with different neural networks to address the am tasks of component detection and relation prediction in randomized controlled trials, and we evaluate this approach on a new huge corpus of 659 abstracts from the medline database. in particular, we extended an existing dataset by annotating 500 abstracts of randomized controlled trials (rct) from the medline database, leading to a dataset of 4198 argument components and 2601 argument relations on different diseases. following the guidelines for the annotation of argument components in rct abstracts provided in @xcite , two annotators with background in computational linguistics carried out the annotation of the 500 abstracts on neoplasm. iaa among the annotators has been calculated on 30 abstracts, resulting in a fleiss' kappa of 0.72 for argumentative components and 0.68 for the more fine-grained distinction between claims and evidence (meaning substantial agreement for both tasks). we carried out the annotation of argumentative relations over the whole dataset of rct abstracts, including both the first version of the dataset @xcite and the newly collected abstracts on neoplasm. iaa has been calculated on 30 abstracts annotated in parallel by three annotators (the same two annotators that carried out the argument component annotation, plus one additional annotator), resulting in a fleiss' kappa of 0.62. the annotation of the remaining abstracts was carried out by one of the above mentioned annotators. we proposed a complete argument mining pipeline for rcts, classifying argument components as evidence and claims, and predicting the relation, i.e., attack or support, holding between those argument components. more precisely, we presented a complete am pipeline for clinical trials relying on deep bidirectional transformers combined with different neural networks, i.e., long short-term memory (lstm) networks, gated recurrent unit (gru) networks, and conditional random fields (crfs). we addressed an extensive evaluation of various am architectures (e.g., for persuasive essays), and obtained a macro f1-score of .87 for component detection and .68 for relation prediction, outperforming current state-of-the-art end-to-end am systems. our evaluation also revealed that current approaches are unable to adequately address the challenges raised by medical text and we show that transformer-based approaches outperform these am pipelines as well as standard baselines.