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Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle
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In: Sensors (Basel) (2022)
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Translational regulation of Chk1 expression by eIF3a via interaction with the RNA-binding protein HuR
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In: Biochem J (2020)
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Lexical richness of Chinese candidates in the graded oral English examinations
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Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking ...
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Additional file 1: of Estimating the prevalence of schistosomiasis japonica in China: a serological approach ...
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Additional file 1: of Estimating the prevalence of schistosomiasis japonica in China: a serological approach ...
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| 10 |
Alkali-silica reaction in waterglass-activated slag mortars incorporating fly ash and metakaolin
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| 11 |
Domain adaptation for statistical machine translation and neural machine translation
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Zhang, Jian. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017
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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)
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Fast gated neural domain adaptation: language model as a case study
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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)
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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
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Keyword:
Machine learning
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URL: http://doras.dcu.ie/23233/
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Refining Image Categorization by Exploiting Web Images and General Corpus ...
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| 14 |
The global view of mRNA-related ceRNA cross-talks across cardiovascular diseases
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Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model
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In: Faculty of Engineering and Information Sciences - Papers: Part B (2017)
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Topic-informed neural machine translation
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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)
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Chinese Relative Clauses Processing in Supportive Context Removing Ambiguity
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In: Studies in Literature and Language; Vol 1, No 4 (2010): Studies in Literature and Language; 12-19 ; 1923-1563 ; 1923-1555 (2010)
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