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 learn that two seemingly (semantically)
unrelated concepts, like leaves and paper are similar due to the similar
rustling sounds they make. Our embeddings prove useful in textual tasks
requiring aural reasoning like text-based sound retrieval and discovering foley
sound effects (used in movies). Moreover, our embedding space captures
interesting dependencies between words and onomatopoeia and outperforms prior
work on aurally-relevant word relatedness datasets such as AMEN and ASLex.

该论文提出了一个新的嵌入方案 (sound-word2vec)，用于学习基于声音的专业词嵌入。 这些嵌入的使用在文本检索和电影制作等任务中表现出了很好的效果，并提出来了许多新的声学和拟声词上的依赖关系。