Datasets:
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README.md
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- logical reasoning
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- soft logic
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- nli
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- natural-language-inference
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- reasoning
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- logic
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# Dataset accompanying the "Probing neural language models for understanding of words of estimative probability" article
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This dataset tests the capabilities of language models to correctly capture the meaning of words denoting probabilities (WEP), e.g. words like "probably", "maybe", "surely", "impossible".
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We used probabilitic soft logic to combine probabilistic statements expressed with WEP (WEP-Reasoning) and we also used the UNLI dataset (https://nlp.jhu.edu/unli/) to directly check whether models can detect the WEP matching human-annotated probabilities according to [Fagen-Ulmschneider, 2018](https://github.com/wadefagen/datasets/tree/master/Perception-of-Probability-Words).
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The dataset can be used as natural language inference data (context, premise, label) or multiple choice question answering (context,valid_hypothesis, invalid_hypothesis).
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- logical reasoning
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- soft logic
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- nli
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- verbal probabilities
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- natural-language-inference
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- reasoning
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- logic
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# Dataset accompanying the "Probing neural language models for understanding of words of estimative probability" article
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This dataset tests the capabilities of language models to correctly capture the meaning of words denoting probabilities (WEP, also called verbal probabilities), e.g. words like "probably", "maybe", "surely", "impossible".
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We used probabilitic soft logic to combine probabilistic statements expressed with WEP (WEP-Reasoning) and we also used the UNLI dataset (https://nlp.jhu.edu/unli/) to directly check whether models can detect the WEP matching human-annotated probabilities according to [Fagen-Ulmschneider, 2018](https://github.com/wadefagen/datasets/tree/master/Perception-of-Probability-Words).
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The dataset can be used as natural language inference data (context, premise, label) or multiple choice question answering (context,valid_hypothesis, invalid_hypothesis).
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