Probabilistic FastText for Multi-Sense Word Embeddings
Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar · 2018
We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information.In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams.This representation allows the model to share statistical strength across sub-word structures (e.g.Latin roots), producing accurate representations of rare, misspelt, or even unseen words.Moreover, each component of the mixture can capture a different word sense.Probabilistic FastText outperforms both FASTTEXT, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets.We also achieve state-ofart performance on benchmarks that measure ability to discern different meanings.Thus, the proposed model is the first to achieve multi-sense representations while having enriched semantics on rare words.