The Current Best of Universal Word Embeddings and Sentence Embeddings

Word and sentence embeddings have become an essential part of any Deep-Learning-based natural language processing systems. They encode words and sentences in fixed-length dense vectors to drastically improve the processing of textual data. A huge trend is the quest for Universal Embeddings: embeddings that are pre-trained on a large corpus and can be plugged in a variety of downstream task models (sentimental analysis, classification, translation…) to automatically improve their performance by incorporating some general word/sentence representations learned on the larger dataset. Transfer learning has been recently shown to drastically increase the performance of NLP models on important tasks such as text classification. Go check the very nice work of Jeremy Howard and Sebastian Ruder (ULMFiT) to see it in action.