Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence
This addresses the challenge of polysemy in word embeddings for natural language processing applications, but appears incremental as it builds on existing Gaussian mixture and KL divergence methods.
The paper tackled the problem of learning word representations by modeling them as multi-sense Gaussian mixture distributions to capture uncertainty and polysemy, and proposed using an approximate Kullback-Leibler divergence objective, with experiments on benchmark datasets showing effectiveness.
Learning word representations has garnered greater attention in the recent past due to its diverse text applications. Word embeddings encapsulate the syntactic and semantic regularities of sentences. Modelling word embedding as multi-sense gaussian mixture distributions, will additionally capture uncertainty and polysemy of words. We propose to learn the Gaussian mixture representation of words using a Kullback-Leibler (KL) divergence based objective function. The KL divergence based energy function provides a better distance metric which can effectively capture entailment and distribution similarity among the words. Due to the intractability of KL divergence for Gaussian mixture, we go for a KL approximation between Gaussian mixtures. We perform qualitative and quantitative experiments on benchmark word similarity and entailment datasets which demonstrate the effectiveness of the proposed approach.