From Word Embeddings to Large Vocabulary Neural Machine Translation
In this thesis, we examine some properties of word embeddings and propose a technique to handle large vocabularies in neural machine translation. We first look at a well-known analogy task and examine the effect of position-dependent weights, the choice of combination function and the impact of supervised learning. We then show that simple embeddings learnt with translational contexts can match or surpass the state of the art on the TOEFL synonym detection task and on the recently introduced SimLex-999 word similarity gold standard. Finally, motivated by impressive results obtained by small-vocabulary (30,000 words) neural machine translation embeddings on some word similarity tasks, we present a GPU-friendly approach to increase the vocabulary size by more than an order of magnitude. Despite originally being developed for obtaining the embeddings only, we show that this technique actually works quite well on actual translation tasks, especially for English to French (WMT'14).
Word2vec TOEFL SimLex-999 GPU WMT Speed-up Deep learning GPU-friendly