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A Generic NLI approach for Classification of Sentiment Associated with Therapies

arXiv 2312.03737 ↗First posted 28 Nov 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

This paper describes our system for addressing SMM4H 2023 Shared Task 2 on "Classification of sentiment associated with therapies (aspect-oriented)". In our work, we adopt an approach based on Natural language inference (NLI) to formulate this task as a sentence pair classification problem, and train transformer models to predict sentiment associated with a therapy on a given text. Our best model achieved 75.22% F1-score which was 11% (4%) more than the mean (median) score of all teams' submissions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 28 Nov 2023.

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References · 6

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  6. 2019Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approachescited by 65

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