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Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color ...
The 2021 Conference on Empirical Methods in Natural Language Processing 2021
;
Abdou, Mostafa
;
Frank, Stella
;
Hershcovich, Daniel
;
Kulmizev, Artur
;
Pavlick, Ellie
;
Søgaard, Anders
. - : Underline Science Inc., 2021
Abstract:
Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases --- (Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric. Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on ...
Keyword:
Computational Linguistics
;
Language Models
;
Machine Learning
;
Machine Learning and Data Mining
;
Natural Language Processing
URL:
https://dx.doi.org/10.48448/crve-w605
https://underline.io/lecture/39853-can-language-models-encode-perceptual-structure-without-groundingquestion-a-case-study-in-color
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