Better Word Embeddings by Disentangling Contextual n-Gram Information
This work addresses the need for better stand-alone word representations in NLP, though it appears incremental as it builds on existing embedding methods.
The paper tackled the problem of improving word embeddings by jointly training them with bigram and trigram embeddings to remove contextual information, resulting in significant performance gains on various tasks.
Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word embeddings along with higher n-gram embeddings helps in the removal of the contextual information from the unigrams, resulting in better stand-alone word embeddings. We empirically show the validity of our hypothesis by outperforming other competing word representation models by a significant margin on a wide variety of tasks. We make our models publicly available.