Findings of the Second Workshop on Neural Machine Translation and Generation
It provides an overview of current topics and benchmarks in neural machine translation for researchers in the field, but is incremental as it documents workshop findings.
The paper summarizes research trends from the Second Workshop on Neural Machine Translation and Generation, highlighting interests in linguistic structure and domain adaptation, and reports results from a shared task focused on creating accurate and efficient machine translation systems.
This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop's shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.