Using Multi-Sense Vector Embeddings for Reverse Dictionaries
This work addresses the challenge of handling polysemy in NLP tasks like reverse dictionaries, offering an incremental improvement over existing methods.
The paper tackled the problem of using multi-sense word embeddings for reverse dictionaries, proposing an attention-based integration method that achieved large improvements in performance.
Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well.