Repository logo
 

An Operation Sequence Model for Explainable Neural Machine Translation

Published version
Peer-reviewed

Type

Conference Object

Change log

Authors

Byrne, Bill 

Abstract

We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.

Description

Keywords

cs.CL, cs.CL

Journal Title

EMNLP BlackboxNLP workshop 2018

Conference Name

EMNLP BlackboxNLP workshop 2018

Journal ISSN

Volume Title

Publisher

Rights

All rights reserved
Sponsorship
EPSRC (1632937)
Engineering and Physical Sciences Research Council (EP/L027623/1)