[PDF] Top 20 Learning to Actively Learn Neural Machine Translation
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Learning to Actively Learn Neural Machine Translation
... Meta-AL learning Several meta-AL ap- proaches have been proposed to learn the AL selection strategy automaticclay from ...reinforcement learning framework (Yue et ...active learning algorithm ... See full document
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Transfer Learning for Low Resource Neural Machine Translation
... transfer learning and explains how we use it to im- prove machine translation ...the learning curves of transfer and no-transfer models, showing that transfer solves an overfitting problem, ... See full document
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Meta Learning for Low Resource Neural Machine Translation
... Resource Translation NMT is known to easily over-fit and result in an inferior performance when the training data is limited (Koehn and Knowles, ...resource translation: (1) utilizing the resource of ... See full document
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Learning to Translate in Real time with Neural Machine Translation
... simultaneous translation, ac- curacy of standard MT systems has greatly im- proved with the introduction of neural-network- based MT systems (NMT) (Sutskever et ...ous translation either through ... See full document
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Competence based Curriculum Learning for Neural Machine Translation
... large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch ...curriculum ... See full document
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Ensemble Learning for Multi Source Neural Machine Translation
... the translation experiments the beam decoding size was set to ...ensemble translation, we are interested in how diverse the translation systems are in their ... See full document
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Learning to Parse and Translate Improves Neural Machine Translation
... Neural Machine Translation (NMT) has enjoyed impressive success without relying on much, if any, prior linguistic knowledge. Some of the most recent studies have for instance demonstrated that NMT ... See full document
7
Imitation Learning for Non Autoregressive Neural Machine Translation
... Non-autoregressive translation models (NAT) have achieved impressive inference ...imitation learning framework for non- autoregressive machine translation, which still enjoys the fast ... See full document
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Learning to Stop in Structured Prediction for Neural Machine Translation
... BSO relies on unnormalized raw scores instead of locally-normalized probabilities to get rid of the label bias problem. However, since the raw score can be either positive or negative, the optimal stop- ping criteria ... See full document
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Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... Bolukbasi et al. (2016) uses a set of words to define the gender direction and to neutralize and equalize the bias from the word vectors. Three set of words are used: One set of ten pairs of words such as woman-man, ... See full document
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Improving Robustness of Neural Machine Translation with Multi task Learning
... output of both encoder and the first decoder. The objective of the first decoder, namely the denois- ing decoder, is to recover from the noisy sentence and generate the corresponding clean sentence. Given both the noisy ... See full document
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Demonstration of a Neural Machine Translation System with Online Learning for Translators
... SDL allows the development of plugins for Tra- dos Studio to enhance and extend the tool. More- over, it has a large developer community 3 helping the software with add-ons and apps. We incorpo- rated our adaptive ... See full document
5
A Multi Task Architecture on Relevance based Neural Query Translation
... proaches learn high dimensional dense representa- tions for words and their objective functions aim to capture contextual information around a ...simpler learning approach that is suitable for our ... See full document
6
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
... We examine the effects of particular order- ings of sentence pairs on the on-line train- ing of neural machine translation (NMT). We focus on two types of such order- ings: (1) ensuring that each ... See full document
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Curriculum Learning for Domain Adaptation in Neural Machine Translation
... curriculum learning is ap- ...curriculum learning on new tasks; a potential direction for fu- ture work may be a curriculum that considers mul- tiple similarity scores ... See full document
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Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... and Machine Translation (MT) make use of the knowledge and expertise of professional translators and interpreters in order to build and improve models for automatic translation or for developing more ... See full document
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Semi Supervised Learning for Neural Machine Translation
... However, most existing NMT approaches suf- fer from a major drawback: they heavily rely on parallel corpora for training translation mod- els. This is because NMT directly models the probability of a ... See full document
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A Machine Learning Approach to the Automatic Evaluation of Machine Translation
... a machine learning approach to evaluating the well- formedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from ... See full document
8
What do Neural Machine Translation Models Learn about Morphology?
... to learn character n-gram patterns that are important for identifying word structure, but as the word be- comes more frequent the word-based model has seen enough examples to make a ... See full document
12
Active Learning for Interactive Neural Machine Translation of Data Streams
... The translation of large data streams is a problem that has been thoroughly ...2017), learning from user post-edits. This incremental learning has also been applied to IMT, either to phrase-based ... See full document
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