Learned in Translation: Contextualized Word Vectors — mentioned by jph00

Sat, 21 Oct 2017 23:59:40 GMT

Word embeddings (or subword embeddings) are used for nearly all recent NLP algorithms, both shallow (e.g. FastText) and deep (e.g. Google Neural Translation). Unless you're using a basic bag-of-words approach, you need to translate your words into some vector format, so you probably want some kind of embeddings. In practice, all the state of the art approaches for translation, language modeling, classification (eg sentiment analysis), etc. all sit on top of embeddings. It's not word embeddings job to handle phrases - but nearly all modern phrase embedding algorithms sit on top of word…

Paper and discussion history. A mention is not an endorsement.

Learned in Translation: Contextualized Word Vectors · HackerPapers discussions

Learned in Translation: Contextualized Word Vectors — mentioned by jph00