Matthew Roselli


Assessing the Ability of Neural Machine Translation Models to Perform Syntactic Rewriting
Jahkel Robin | Alvin Grissom II | Matthew Roselli
Proceedings of the 2019 Workshop on Widening NLP

We describe work in progress for evaluating performance of sequence-to-sequence neural networks on the task of syntax-based reordering for rules applicable to simultaneous machine translation. We train models that attempt to rewrite English sentences using rules that are commonly used by human interpreters. We examine the performance of these models to determine which forms of rewriting are more difficult for them to learn and which architectures are the best at learning them.