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Pearson correlation between predicted and true variables for each model. We plot each crossvalidation split as a separate scatter point. XGLM consistently provides the best correlation scores across all variables.

Pearson correlation between predicted and true variables for each model. We plot each crossvalidation split as a separate scatter point. XGLM consistently provides the best correlation scores across all variables.

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Figure 1: Distribution of concreteness and sensory strength ratings for...
Figure 2: Pearson correlation between predicted and true variables for...
Figure 3: Sense discrimination scores for each model, using all...
Figure 4: Overall sense discrimination scores for a number of...
Figure 5: Pairwise similarities as measured by Representational...
Polysemy through the lens of psycholinguistic variables: a dataset and an evaluation of static and contextualized language models
Conference Paper
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  • Jun 2024
Polysemes are words that can have different senses depending on the context of utterance: for instance, 'newspaper' can refer to an organization (as in 'manage the newspaper') or to an object (as in 'open the newspaper'). Contrary to a large body of evidence coming from psy-cholinguistics, polysemy has been traditionally modelled in NLP by assuming...
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Context 1
... Figure 2 we report the average Pearson correlation between predicted and real ratings. XGLM (best performing layer and version: layer 7 of XGLM-1.7B; ...