Filipe Dias


2023

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Team INF-UFRGS at SemEval-2023 Task 7: Supervised Contrastive Learning for Pair-level Sentence Classification and Evidence Retrieval
Abel Corrêa Dias | Filipe Dias | Higor Moreira | Viviane Moreira | João Luiz Comba
Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)

This paper describes the EvidenceSCL system submitted by our team (INF-UFRGS) to SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT). NLI4CT is divided into two tasks, one for determining the inference relation between a pair of statements in clinical trials and a second for retrieving a set of supporting facts from the premises necessary to justify the label predicted in the first task. Our approach uses pair-level supervised contrastive learning to classify pairs of sentences. We trained EvidenceSCL on two datasets created from NLI4CT and additional data from other NLI datasets. We show that our approach can address both goals of NLI4CT, and although it reached an intermediate position, there is room for improvement in the technique.