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.