Annotation Difficulties in Natural Language Inference

  • Aikaterini-Lida Kalouli LMU
  • Livy Real Americanas S. A.
  • Annebeth Buis University of Colorado
  • Martha Palmer University of Colorado
  • Valeria de Paiva Topos Institute


State-of-the-art models have obtained high accuracy on mainstream Natural Language Inference (NLI) datasets. However, recent research has suggested that the task is far from solved. Current models struggle to generalize and fail to consider the inherent human disagreements in tasks such as NLI. In this work, we conduct an experiment based on a small subset of the NLI corpora such as SNLI and SICK. It reveals that some inference cases are inherently harder to annotate than others, although good-quality guidelines can reduce this difficulty to some extent. We propose adding a Difficulty Score to NLI datasets, to capture the human difficulty level of agreement.


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KALOULI, Aikaterini-Lida; REAL, Livy; BUIS, Annebeth; PALMER, Martha; PAIVA, Valeria de. Annotation Difficulties in Natural Language Inference. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 247-254. DOI: