msadat3/SciNLI
The dataset and code for ACL 2022 paper "SciNLI: A Corpus for Natural Language Inference on Scientific Text" are released here.
This project provides a specialized dataset and tools for evaluating how well AI models understand the logical relationships between sentences in scientific papers. It takes pairs of sentences from NLP and computational linguistics research papers and labels their semantic relationship (e.g., entailment, contradiction, neutral). Researchers and developers working on AI models for scientific text analysis would use this to benchmark and improve their models' comprehension.
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Use this if you are developing or evaluating Natural Language Understanding (NLU) models specifically for scientific literature and need a benchmark dataset tailored to the unique language and structure of academic papers.
Not ideal if your NLU tasks involve general conversational or everyday language, as this dataset is highly specialized for formal scientific text.
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Oct 17, 2023
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