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Sound-Word2Vec: Learning Word Representations Grounded in Sounds
[article]
2017
arXiv
pre-print
To be able to interact better with humans, it is crucial for machines to understand sound - a primary modality of human perception. Previous works have used sound to learn embeddings for improved generic textual similarity assessment. In this work, we treat sound as a first-class citizen, studying downstream textual tasks which require aural grounding. To this end, we propose sound-word2vec - a new embedding scheme that learns specialized word embeddings grounded in sounds. For example, we
arXiv:1703.01720v4
fatcat:cy652b52sjfm7e7zi26ex23spe