@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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T I2R at SemEval-2023 Task 7: Explanations-driven Ensemble Approach for Natural Language Inference over Clinical Trial Data
%A Rajamanickam, Saravanan
%A Rajaraman, Kanagasabai
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F rajamanickam-rajaraman-2023-i2r
%X 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.
%R 10.18653/v1/2023.semeval-1.226
%U https://aclanthology.org/2023.semeval-1.226
%U https://doi.org/10.18653/v1/2023.semeval-1.226
%P 1630-1635
Markdown (Informal)
[I2R at SemEval-2023 Task 7: Explanations-driven Ensemble Approach for Natural Language Inference over Clinical Trial Data](https://aclanthology.org/2023.semeval-1.226) (Rajamanickam & Rajaraman, SemEval 2023)
ACL