@inproceedings{takehana-etal-2023-stanford,
    title = "{S}tanford {ML}ab at {S}em{E}val 2023 Task 7: Neural Methods for Clinical Trial Report {NLI}",
    author = "Takehana, Conner  and
      Lim, Dylan  and
      Kurtulus, Emirhan  and
      Iyer, Ramya  and
      Tanimura, Ellie  and
      Aggarwal, Pankhuri  and
      Cantillon, Molly  and
      Yu, Alfred  and
      Khan, Sarosh  and
      Chi, Nathan",
    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-1.245",
    doi = "10.18653/v1/2023.semeval-1.245",
    pages = "1769--1775",
    abstract = "We present a system for natural language inference in breast cancer clinical trial reports, as framed by SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. In particular, we propose a suite of techniques for two related inference subtasks: entailment and evidence retrieval. The purpose of the textual entailment identification subtask is to determine the inference relation (either entailment or contradiction) between given statement pairs, while the goal of the evidence retrieval task is to identify a set of sentences that support this inference relation. To this end, we propose fine-tuning Bio+Clinical BERT, a BERT-based model pre-trained on clinical data. Along with presenting our system, we analyze our architectural decisions in the context of our model{'}s accuracy and conduct an error analysis. Overall, our system ranked 20 / 30 on the entailment subtask.",
}

We present a system for natural language inference in breast cancer clinical trial reports, as framed by SemEval 2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. In particular, we propose a suite of techniques for two related inference subtasks: entailment and evidence retrieval. The purpose of the textual entailment identification subtask is to determine the inference relation (either entailment or contradiction) between given statement pairs, while the goal of the evidence retrieval task is to identify a set of sentences that support this inference relation. To this end, we propose fine-tuning Bio+Clinical BERT, a BERT-based model pre-trained on clinical data. Along with presenting our system, we analyze our architectural decisions in the context of our model{'}s accuracy and conduct an error analysis. Overall, our system ranked 20 / 30 on the entailment subtask.
Takehana, Conner Lim, Dylan Kurtulus, Emirhan Iyer, Ramya Tanimura, Ellie Aggarwal, Pankhuri Cantillon, Molly Yu, Alfred Khan, Sarosh Chi, Nathan
Stanford MLab at SemEval 2023 Task 7: Neural Methods for Clinical Trial Report NLI