13 |
Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
|
|
|
|
BASE
|
|
Show details
|
|
19 |
A Psycholinguistic Model for the Marking of Discourse Relations
|
|
|
|
In: Dialogue & Discourse; Vol 8 No 1 (2017); 106-131 ; 2152-9620 (2017)
|
|
BASE
|
|
Show details
|
|
20 |
Topic-informed neural machine translation
|
|
|
|
In: Zhang, Jian, Li, Liangyou orcid:0000-0002-0279-003X , Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2016) Topic-informed neural machine translation. In: 26th International Conference on Computational Linguistics, 13-16 Dec 2016, Osaka, Japan. (2016)
|
|
Abstract:
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.
|
|
Keyword:
Machine translating
|
|
URL: http://doras.dcu.ie/23220/
|
|
BASE
|
|
Hide details
|
|
|
|