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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Domain adaptation for statistical machine translation and neural machine translation Zhang, Jian. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017 In: Zhang, Jian orcid:0000-0001-5659-5865 (2017) Domain adaptation for statistical machine translation and neural machine translation. PhD thesis, Dublin City University. (2017) BASE Show details 2 Fast gated neural domain adaptation: language model as a case study Liu, Qun; Wu, Xiaofeng; Way, Andy; Zhang, Jian In: Zhang, Jian orcid:0000-0001-5659-5865 , Wu, Xiaofeng, Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2017) Fast gated neural domain adaptation: language model as a case study. In: Proceedings of FETLT 2016: Future and Emerging Trends in Language Technologies, Machine Learning and Big Data, 30 Nov- 2 Dec 2016, Seville, Spain. (2017) Abstract: Neural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a novel domain adaptation mechanism in neural network training. Instead of learning and adapting the neural network on millions of training sentences – which can be very timeconsuming or even infeasible in some cases – we design a domain adaptation gating mechanism which can be used in recurrent neural networks and quickly learn the out-of-domain knowledge directly from the word vector representations with little speed overhead. In our experiments, we use the recurrent neural network language model (LM) as a case study. We show that the neural LM perplexity can be reduced by 7.395 and 12.011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively. Furthermore, we show that using the domain-adapted neural LM to re-rank the statistical machine translation n-best list on the French-to-English language pair can significantly improve translation quality Keyword: Machine learning URL: http://doras.dcu.ie/23233/ BASE Hide details

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