Discrete Structural Planning for Generating Diverse Translations

Raphael Shu, Hideki Nakayama

Abstract: Planning is important for humans when producing complex languages, which is a missing part in current language generation models. In this work, we add a planning phase in neural machine translation to control the global sentence structure ahead of translation. Our approach learns discrete structural representations to encode syntactic information of target sentences. During translation, we can either let beam search to choose the structural codes automatically or specify the codes manually. The word generation is then conditioned on the selected discrete codes. Experiments show that the translation performance remains intact by learning the codes to capture pure structural variations. Through structural planning, we are able to control the global sentence structure by manipulating the codes. By evaluating with a proposed structural diversity metric, we found that the sentences sampled using different codes have much higher diversity scores. In qualitative analysis, we demonstrate that the sampled paraphrase translations have drastically different structures.

Keywords: machine translation, syntax, diversity, code learning

TL;DR: Learning discrete structural representation to control sentence generation and obtain diverse outputs