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TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report Analysis

arXiv 2404.09136 ↗First posted 14 Apr 2024cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

This paper introduces novel methodologies for the Natural Language Inference for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated clinical-Language summaries for DeBERTa Report Analysis) which incorporates T5-model generated premise summaries for improved entailment and contradiction analysis in clinical NLI tasks. This approach overcomes the challenges posed by small context windows and lengthy premises, leading to a substantial improvement in Macro F1 scores: a 0.184 increase over truncated premises. Our comprehensive experimental evaluation, including detailed error analysis and ablations, confirms the superiority of TLDR in achieving consistency and faithfulness in predictions against semantically altered inputs.

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References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  4. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  5. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  6. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  7. 2024SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trialscited by 20
  8. 2023NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reportscited by 18
  9. 2023ViDeBERTa: A powerful pre-trained language model for Vietnamesecited by 11
  10. 2023Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reportscited by 9
  11. 2023THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrievalcited by 4

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