[PDF] Top 20 Neural Machine Translation with Adequacy-Oriented Learning
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Neural Machine Translation with Adequacy-Oriented Learning
... Length Analysis We group sentences of similar lengths and compute both the BLEU score and C DR score for each group, as shown in Figure 3. The four length spans contain 1386, 2284, 1285, and 498 sentences, respectively. ... See full document
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Exploring Adequacy Errors in Neural Machine Translation with the Help of Cross Language Aligned Word Embeddings
... by translation on the one hand and unrelated in-domain and out-of-domain sentences on the other, which means that the analysis of sub- tle adequacy issues frequently observed in NMT, such as omissions or ... See full document
7
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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Curriculum Learning for Domain Adaptation in Neural Machine Translation
... The total amount of the in-domain data in each domain is summarized in Table 3. In this paper, we uniformly sample 15k in-domain data from each dataset. We choose the amount of 15k, which makes up a relatively small ... See full document
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Predicting Machine Translation Adequacy with Document Embeddings
... dependent. Machine translation evalua- tion metrics generally provide a shallow compari- son between hypotheses and reference translations focusing on capturing the grammatical similari- ties between the ... See full document
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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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Learning to Stop in Structured Prediction for Neural Machine Translation
... Sequence-to-sequence (seq2seq) models based on RNNs (Sutskever et al., 2014; Bahdanau et al., 2014), CNNs (Gehring et al., 2017) and self- attention (Vaswani et al., 2017) have achieved great successes in Neural ... See full document
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Competence based Curriculum Learning for Neural Machine Translation
... to 1 if its condition is satisfied and 0 otherwise. Next we need to decide how to aggregate the rel- ative word frequencies of all words in a sentence to obtain a single difficulty score for that sentence. Previous ... See full document
11
Transfer Learning for Low Resource Neural Machine Translation
... We also use the NMT model with transfer learn- ing as a feature when re-scoring output n-best lists (n = 1000) from the SBMT system. Table 3 shows the results of re-scoring. We compare re-scoring with transfer NMT to ... See full document
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Demonstration of a Neural Machine Translation System with Online Learning for Translators
... online learning for neural machine translation in a production environ- ...our machine translation servers to one of the most com- mon user interfaces for professional transla- ... See full document
5
A Multi Task Architecture on Relevance based Neural Query Translation
... multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query ...task learning architecture that achieves ... See full document
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Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder
... the neural ma- chine translation (NMT) architecture sim- pler, yet elegant compared to traditional statistical machine translation ...for learning each of these phe- ...joint-data ... See full document
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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
... reinforcement learning for select- ing examples in a co-trained classifier (Wu et ...imitation learning to actively select monolingual training sentences for labeling in NMT, and show that the learned ... See full document
8
Trivial Transfer Learning for Low Resource Neural Machine Translation
... Furthermore, the improvement is not restricted only to related languages as Estonian and Finnish as shown in previous works. Unrelated language pairs (shown in bold in Table 2) like Czech and Estonian work too and in ... See full document
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Learning from Chunk based Feedback in Neural Machine Translation
... An example where the NMT system with chunk- based feedback yields a better translation in com- parison to other systems is the German sentence “Die Krise ist vor¨uber.” (“The crisis is over.“). The German word ... See full document
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Improving Robustness of Neural Machine Translation with Multi task Learning
... Clean fr: To make our back-translation strategy more generalized to settings where the above par- allel data is not enough to train the model, we also design a pipeline to utilize monolingual data which is likely ... See full document
7
Multi agent Learning for Neural Machine Translation
... many-to-many learning to the one-to-many (one teacher vs. many students) learning, extending ensemble knowledge distillation (Fukuda et ...by learning from the ensemble model (Teacher) of all agents ... See full document
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Active Learning for Interactive Neural Machine Translation of Data Streams
... active learning techniques to the translation of unbounded data streams via interactive neural machine ...the neural machine translation ...a neural machine ... See full document
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NICT’s participation to WAT 2019: Multilingualism and Multi step Fine Tuning for Low Resource NMT
... Neural machine translation (NMT) (Cho et ...alignments, translation rules, and complicated decoding algorithms, which are the characteris- tics of phrase-based statistical machine ... See full document
5
Meta Learning for Low Resource Neural Machine Translation
... resource machine translation is that the ap- proach outlined above assumes the input and out- put spaces are shared across all the source and tar- get ...chine translation in general due to the ... See full document
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