Creating linguistic embedding space for odors

Abstract

To obtain a genuine meaning for a natural language sentence, it is necessary to understand the connection between words or phrases in a language and various kinds of real-world information. One of such real-world information might be odors. Previous studies investigated whether word embeddings from word2vec can acquire odor information. However, their model, trained with general corpora, does not have much odor information due to a small volume of corpora related to odors. In this paper, we propose TOLE, Thesaurus-enhanced Odor-adaptive Linguistic Embeddings. TOLE retains the odor information with domain adaptation and word-level contrastive learning on pre-trained language models. As a result, TOLE can improve the similarity between odor embeddings from odor descriptors and linguistic embeddings.