What are Context Vectors (CoVe)?
Context Vectors (CoVe) are word representations generated by a pre-trained deep learning model for machine translation. CoVe aims to capture both semantic and syntactic information from the input text by leveraging the context from the source and target languages. CoVe has been shown to improve performance on various natural language processing tasks, such as sentiment analysis, named entity recognition, and question answering. CoVe are typically used as additional input features alongside other word embeddings, such as GloVe or Word2Vec, to enhance the performance of downstream NLP models.
How are CoVe generated?
CoVe are generated by training a sequence-to-sequence model with attention, typically an encoder-decoder architecture, on a large parallel corpus of source and target language sentences. The encoder part of the model, usually a recurrent neural network (RNN) or a transformer, captures the contextual information of the input sentences. The hidden states of the encoder are then used as the CoVe representations for the input words.
Resources for learning more about CoVe
To learn more about CoVe and its applications, you can explore the following resources:
Learned in Translation: Contextualized Word Vectors, the original paper introducing CoVe
Deep contextualized word representations, a blog post discussing CoVe
Using pre-trained word embeddings in a Keras model, a tutorial on how to use pre-trained word embeddings, including CoVe, in a Keras model
Saturn Cloud, a platform for free cloud compute resources to explore CoVe and other NLP techniques
