Sound-Word2Vec: Learning Word Representations Grounded in Sounds
Table 2: Comparison to state of the art AMEN and ASLex datasets (Kiela and Clark, 2015) (higher is better). Our approach performs better than Kiela and Clark (2015).
['Embedding', 'Spearman Correlation  [ITALIC] ρs AMEN', 'Spearman Correlation  [ITALIC] ρs ASLex']
In addition to enlarging the vocabulary, the pre-training helps induce smoothness in the sound-word2vec embeddings – allowing us to transfer semantics learnt from sounds to words that were not present as tags in the Freesound database. Indeed, we find that word2vec pre-training helps improve performance (Sec.