Registry
Module Specifications
Archived Version 2022 - 2023
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Description This course introduces the fundamentals of machine translation, including the currently widely used neural approach. | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Discuss the challenges associated with machine translation including its evaluation. 2. Explain the concept of machine translation including approaches and the importance of language data. 3. Demonstrate how a statistical translation model can be inferred from a parallel corpus of texts using unsupervised machine learning techniques. 4. Explain how neural networks work in general and how they can be used for language-related tasks. 5. Explain the concepts of statistical language modelling and neural language modelling and their differences. 6. Explain the decoding process in NMT and understand the differences between decoding in SMT and decoding in NMT. 7. Demonstrate a knowledge of the state-of-the-art transformer neural machine translation. 8. Explain the differences between recurrent machine translation and transformer machine translation. 9. Train, test and evaluate MT system using the open-source Joey NMT tookit. | |||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities Introduction to Machine TranslationWhat is machine translation? Overview of the three approaches: rule-based, statistical, neural. Importance of data for statistical and neural MT. Sentence alignment and preprocessing.Evaluating MT systemsThe relative advantages and disadvantages of human evaluation and automatic evaluation. Two main concepts used for automatic evaluation metrics: n-gram matching and edit distance.Statistical Machine TranslationProbability model for translation, Translation model and Language model, Word Alignments and IBM models, Phrase-based SMT, Decoding.Introduction to Neural NetworksWhat are neural networks? Architectures: feed forward and recurrent networks. Training neural networks: back-propagation and gradient descent.Neural Language ModelsWord representations: why are they needed? Different types: one-hot, static, contextual, external vs internal representations. Feed-forward neural language models. Recurrent neural language models.Neural Machine TranslationEncoder-decoder architecture and sequence-to-sequence modelling. Decoding for NMT. Recurrent neural networks for MT. Recurrent neural MT with attention. Transformer neural networks for MT. | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources None | |||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||
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