Context-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model
release_oshtdoby35cwvps2igy42tqtxq
by
Amane Sugiyama, Naoki Yoshinaga
2020
Abstract
Although many context-aware neural machine translation models have been
proposed to incorporate contexts in translation, most of those models are
trained end-to-end on parallel documents aligned in sentence-level. Because
only a few domains (and language pairs) have such document-level parallel data,
we cannot perform accurate context-aware translation in most domains. We
therefore present a simple method to turn a sentence-level translation model
into a context-aware model by incorporating a document-level language model
into the decoder. Our context-aware decoder is built upon only a sentence-level
parallel corpora and monolingual corpora; thus no document-level parallel data
is needed. In a theoretical viewpoint, the core part of this work is the novel
representation of contextual information using point-wise mutual information
between context and the current sentence. We show the effectiveness of our
approach in three language pairs, English to French, English to Russian, and
Japanese to English, by evaluation in bleu and contrastive tests for
context-aware translation.
In text/plain
format
Archived Files and Locations
application/pdf 610.5 kB
file_lfed6hpqxnhnlcciciacvpxzo4
|
arxiv.org (repository) web.archive.org (webarchive) |
2010.12827v1
access all versions, variants, and formats of this works (eg, pre-prints)