In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine
translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters
to maximize the conditional probability of target sentences given source sentences. In this paper,
we present a novel approach to improve the translation performance in NMT by conveying topic
knowledge during translation. The proposed topic-informed NMT can increase the likelihood of
selecting words from the same topic and domain for translation. Experimentally, we demonstrate
that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute
improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005
test sets, respectively, compared to NMT without topic information.
Matsumoto, Yuji and Prasad, Rashmi, (eds.)
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers.
.
The COLING 2016 Organizing Committee.
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ADAPT Centre for Digital Content Technology (www.adaptcentre.ie) at Dublin City University is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:
23220
Deposited On:
01 May 2019 15:32 by
Thomas Murtagh
. Last Modified 01 May 2019 15:32