Word Embeddings are vector representations of words in a continuous vector space, where words with similar meanings are represented by similar vectors. These embeddings capture semantic relationships between words, allowing algorithms to understand the contextual similarities and differences in language. Word embeddings have become an essential component in natural language processing (NLP) and machine learning applications, including sentiment analysis, machine translation, named entity recognition, and document clustering. The dense and meaningful representations provided by word embeddings contribute to the improved performance of various language-related tasks.


Overcoming Poor Word Embeddings with Word Definitions

Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models. Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap. Our model’s understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.