8 Ways to Perform NLP Better in 2022

Machine translation (MT) has become ubiquitous as a technology that enables individuals to access content on-demand in real-time that is written in languages they do not speak. However, contrary to recent press releases that have said it has surpassed human quality, the results in practice suggest that it has a long way to go. One of the biggest challenges current-generation neural MT (NMT) faces is that its engines are not easily adaptable and cannot respond to context or extra-linguistic knowledge that human translators routinely deal with. In addition, NMT's improvements have largely been in terms of fluency (how natural the output sounds) rather than accuracy (how well the translated text represents the content of the source text). This discrepancy in improvement actually increases the risk that critical errors may remain undetected simply because they are readable and sound plausible. The next step forward is to build "responsive MT": systems that can take advantage of embedded metadata about a wide variety of topics and use them to preferentially use the most relevant training data.