[PDF] Top 20 Ensemble Learning for Multi Source Neural Machine Translation
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Ensemble Learning for Multi Source Neural Machine Translation
... each ensemble set type, we evaluate ensembles of size 2 and ...single-source ensemble of l German-English ...a multi-source ensemble of size 2, and we use subscript indices if ... See full document
10
Improving a Multi Source Neural Machine Translation Model with Corpus Extension for Low Resource Languages
... In machine translation, we often try to collect resources to improve ...train machine translation ...a multi-source neural machine translation ...of ... See full document
5
A Study of Reinforcement Learning for Neural Machine Translation
... SOTA translation quality in several oth- er datasets. For En-De translation, we utilize the transformer base v1 ...En-Zh translation to be ...initial learning rate is 0.1, and we fol- low the ... See full document
10
Learning to Actively Learn Neural Machine Translation
... NMT Model Our baseline model consists of a 2-layer bi-directional LSTM encoder with an em- beddings size of 512 and a hidden size of 512. The 1-layer LSTM decoder with 512 hidden units uses an attention network with 128 ... See full document
11
Semi Supervised Learning for Neural Machine Translation
... semi-supervised learning for neural machine ...trains source-to-target and target-to-source trans- lation ...the source-to-target and target-to-source models serve as the ... See full document
10
Zero Resource Translation with Multi Lingual Neural Machine Translation
... of source sen- tences in Spanish (Es) and French (Fr) to English ...any multi-way parallel ...the translation quality (mea- sured in BLEU) by 3 points in the case of the test set (compare Table 2 ... See full document
10
OpenNMT: Open Source Toolkit for Neural Machine Translation
... Multi-GPU OpenNMT additionally supports multi-GPU training using data parallelism. Each GPU has a replica of the master parameters and process independent batches during training phase. Two modes are ... See full document
6
Learning to Copy for Automatic Post Editing
... of machine trans- lation systems in a post-processing step, is an important task in natural language ...using neural networks, how to model the copying mechanism for APE remains a ...identify ... See full document
11
Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... The main drawback of this approach is that the whole source sentence has to be stored in a fixed- size context vector. To overcome this problem, Bahdanau et al. (2014) introduced the soft atten- tion mechanism. ... See full document
10
Neural Machine Translation with Adequacy-Oriented Learning
... a translation case in Figure ...two translation results from the RNNSearch and Adequacy-NMT models respectively, as well as the corresponding C DR and BLEU ...different translation quality. As seen, ... See full document
8
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
... adding source hierarchical informa- tion to neural machine translation has used super- vised ...a multi-task setup with a shared encoder to parse and trans- late the source ... See full document
7
Multi Granularity Self Attention for Neural Machine Translation
... Representation Multi- granularity representation, which is proposed to make full use of subunit composition at different levels of granularity, has been explored in various NLP tasks, such as paraphrase ... See full document
11
Exploiting Source side Monolingual Data in Neural Machine Translation
... a multi-task learn- ing method for translating one source language into multiple target languages in NMT so that the en- coder network can be shared when dealing with sev- eral sets of bilingual ...(e.g. ... See full document
11
Neural Machine Translation with Source Side Latent Graph Parsing
... Neural Machine Translation (NMT) is an active area of research due to its outstanding empiri- cal results (Bahdanau et ...improve translation accu- racy (Eriguchi et ...supervised ... See full document
11
Incorporating Source Syntax into Transformer Based Neural Machine Translation
... in learning to parse English in a multi-task sys- ...a multi-task system depends on the target language, whereas we saw in the previous sections that the transla- tion success depends more on the ... See full document
10
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi Task Learning Approach
... the source text, which would then be useful for con- veying the correct meaning to the ...example translation, showing that semantic parsing has helped NMT by understanding that “the subject sees the object ... See full document
10
OpenNMT: Neural Machine Translation Toolkit
... released, including GroundHog, Blocks, Nematus, tensorflow-seq2seq, GNMT, fair-seq, Ten- sor2Tensor, Sockeye, Neural Monkey, lamtram, XNMT, SGNMT, and Marian. These projects mostly implement variants of the same ... See full document
8
Multi Source Neural Translation
... first translation by hand, then turns the rest over to machine translation ...The translation system now has two strings as input, which can reduce ambiguity via “triangu- lation” (Kay’s ... See full document
5
A Multi Task Architecture on Relevance based Neural Query Translation
... a multi-task learning approach to train a Neural Machine Translation (NMT) model with a Relevance-based Auxiliary Task (RAT) for search query ...our multi- task learning ... See full document
6
Improving Robustness of Neural Machine Translation with Multi task Learning
... of multi-task learning for machine translation, Tu et ...the source sen- tence from the hidden layers of the translation de- ...plete source information, which helps ... See full document
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