@inproceedings{correa-dias-etal-2023-team,
    title = "Team {INF}-{UFRGS} at {S}em{E}val-2023 Task 7: Supervised Contrastive Learning for Pair-level Sentence Classification and Evidence Retrieval",
    author = "Corr{\^e}a Dias, Abel  and
      Dias, Filipe  and
      Moreira, Higor  and
      Moreira, Viviane  and
      Comba, Jo{\~a}o Luiz",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.96",
    doi = "10.18653/v1/2023.semeval-1.96",
    pages = "700--706",
    abstract = "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.",
}

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.
Corr{\^e}a Dias, Abel Dias, Filipe Moreira, Higor Moreira, Viviane Comba, Jo{\~a}o Luiz
Team INF-UFRGS at SemEval-2023 Task 7: Supervised Contrastive Learning for Pair-level Sentence Classification and Evidence Retrieval