Cong Duy Vu Hoang

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Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from(More)
Recurrent neural network language models (RNNLM) have recently demonstrated vast potential in modelling long-term dependencies for NLP problems, ranging from speech recognition to machine translation. In this work, we propose methods for conditioning RNNLMs on external side information, e.g., metadata such as keywords, description, document title or topic(More)
We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. The resulting optimisation problem is then tackled using constrained gradient optimisation. Our powerful decoding framework, enables decoding intractable models such as the intersection of left-to-right and right-to-left (bidirectional) as well as(More)
2011 Acknowledgements First and foremost, I must thank my advisor, Min-Yen Kan, for all his advice, guidance , and patience in seeing me through my Ph.D. years. Without his generous and unwavering support, I would not have completed this Ph.D. thesis. He heads the Web Information Retrieval / Natural Language Processing Group (WING), and he is known among(More)
This paper presents an extension of neu-ral machine translation (NMT) model to incorporate additional word-level linguistic factors. Adding such linguistic factors may be of great benefits to learning of NMT models, potentially reducing language ambiguity or alleviating data sparseness problem (Koehn and Hoang, 2007). We explore different linguistic(More)
OCR (Optical Character Recognition) scanners do not always produce 100% accuracy in recognizing text documents, leading to spelling errors that make the texts hard to process further. This paper presents an investigation for the task of spell checking for OCR-scanned text documents. First, we conduct a detailed analysis on characteristics of spelling errors(More)
Interactive or Incremental Statistical Machine Translation (IMT) aims to provide a mechanism that allows the statistical models involved in the translation process to be incrementally updated and improved. The source of knowledge normally comes from users who either post-edit the entire translation or just provide the translations for wrongly translated(More)
Crowdsourcing has emerged as a new method for obtaining annotations for training models for machine learning. While many variants of this process exist, they largely differ in their method of motivating subjects to contribute and the scale of their applications. To date, however, there has yet to be a study that helps a practitioner to decide what form an(More)
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