@inproceedings{rajamanickam-rajaraman-2023-i2r,
    title = "{I}2{R} at {S}em{E}val-2023 Task 7: Explanations-driven Ensemble Approach for Natural Language Inference over Clinical Trial Data",
    author = "Rajamanickam, Saravanan  and
      Rajaraman, Kanagasabai",
    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-1.226",
    doi = "10.18653/v1/2023.semeval-1.226",
    pages = "1630--1635",
    abstract = "In this paper, we describe our system for SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. Given a CTR premise, and a statement, this task involves 2 sub-tasks (i) identifying the inference relation between CTR - statement pairs (Task 1: Textual Entailment), and (ii) extracting a set of supporting facts, from the premise, to justify the label predicted in Task 1 (Task 2: Evidence Retrieval). We adopt an explanations driven NLI approach to tackle the tasks. Given a statement to verify, the idea is to first identify relevant evidence from the target CTR(s), perform evidence level inferences and then ensemble them to arrive at the final inference. We have experimented with various BERT based models and T5 models. Our final model uses T5 base that achieved better performance compared to BERT models. In summary, our system achieves F1 score of 70.1{\%} for Task 1 and 80.2{\%} for Task 2. We ranked 8th respectively under both the tasks. Moreover, ours was one of the 5 systems that ranked within the Top 10 under both tasks.",
}

In this paper, we describe our system for SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data. Given a CTR premise, and a statement, this task involves 2 sub-tasks (i) identifying the inference relation between CTR - statement pairs (Task 1: Textual Entailment), and (ii) extracting a set of supporting facts, from the premise, to justify the label predicted in Task 1 (Task 2: Evidence Retrieval). We adopt an explanations driven NLI approach to tackle the tasks. Given a statement to verify, the idea is to first identify relevant evidence from the target CTR(s), perform evidence level inferences and then ensemble them to arrive at the final inference. We have experimented with various BERT based models and T5 models. Our final model uses T5 base that achieved better performance compared to BERT models. In summary, our system achieves F1 score of 70.1{\%} for Task 1 and 80.2{\%} for Task 2. We ranked 8th respectively under both the tasks. Moreover, ours was one of the 5 systems that ranked within the Top 10 under both tasks.
Rajamanickam, Saravanan Rajaraman, Kanagasabai
I2R at SemEval-2023 Task 7: Explanations-driven Ensemble Approach for Natural Language Inference over Clinical Trial Data