Details of the submissions chosen by the ARA do not seem to be publicly available, but their authors’ previous work as well as prior research in the same subject matter can shed some light on the nature of their research.
For NLP, research included “Natural Language Processing for Literary Texts” by David Bamman, Assistant Professor at the University of California, Berkeley. Literary texts are a challenge for both NLP and NMT due to their creative use of language.
There were also a couple of submissions on branches of NLP such as question answering and recommenders, e.g. the systems that recommend the next movies you might want to watch based on your viewing history on Netflix. The former was titled “Question Answering and Reasoning about Product Reviews” by Hannaneh Hajishirzi, Research Assistant Professor and Adjunct Assistant Professor at University of Washington. The latter was “Building Blocks for Natural Language Recommenders” by Max Harper, Research Scientist at University of Minnesota’s Department of Computer Science and Engineering.
There was also NLP research on human-machine interaction using naturally worded commands, namely “Learning to Understand Natural Language Commands on Changing Websites” by Assistant Professor Percy Liang from Stanford University.
And finally, research into multilingual language understanding through a specific method of converting naturally-worded language into a representation that a machine can understand: “Enabling Multilingual Language Understanding: Universal Typed Semantic Parsing” by Stanford University’s Christopher Manning, a Thomas M. Siebel Professor in Machine Learning and Professor of Linguistics and Computer Science.
As for NMT, Senior Research Scientist and Assistant Research Professor Kevin Duh from John Hopkins University submitted “Multi-objective Hyperparameter Search for Fast and Accurate Neural Machine Translation.” The goal for this project is fast, accurate NMT, but Duh’s other work also tackles domain adaptation training and NMT for low-resource settings.
Amazon has been active in the NMT scene recently. Their latest big move was the official unveiling of Amazon Translate, during which they brought Lionbridge on stage with them. Even during its preview launch towards the end of 2017, Amazon proactively pitched Translate to language service providers.
Download the Slator 2019 Neural Machine Translation Report for the latest insights on the state-of-the art in neural machine translation and its deployment.
Slator 2019 Neural Machine Translation Report: Deploying NMT in Operations
32 pages, NMT state-of-the-art, 5 case studies, 30 commentaries, NMT in day-to-day operations
