Issue #86 – Neural MT with Levenshtein Transformer

18 Jun20 Issue #86 – Neural MT with Levenshtein Transformer Author: Dr. Patrik Lambert, Senior Machine Translation Scientist @ Iconic Introduction The standard Transformer model is autoregressive, meaning that the prediction of each target word is based on the predictions for the previous words. The output is generated from left to right, with no chance to revise a past decision and without considering future predictions of the words on the right of the current word. In a recent post (#82), […]

Issue #82 – Constrained Decoding using Levenshtein Transformer

14 May20 Issue #82 – Constrained Decoding using Levenshtein Transformer Author: Raj Patel, Machine Translation Scientist @ Iconic Introduction In constrained decoding, we force in-domain terminology to appear in the final translation. We have previously discussed constrained decoding in earlier blog posts (#7, #9, #79). In this blog post, we will discuss a simple and effective algorithm for incorporating lexical constraints in Neural Machine Translation (NMT) proposed by Susanto et al. (2020) and try to understand how it is better than […]