[PDF] Top 20 Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
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Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... a multi-layer perceptron with 512 hidden units and tanh activa- tion ...applied using log-likelihood of the concatenated validation sets from the considered ... See full document
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Extending hybrid word character neural machine translation with multi task learning of morphological analysis
... Neural machine translation provides another way to utilize external annotations, multi-task learning ...chine learning approach that aims at improving the generalization ... See full document
7
Adaptive Knowledge Sharing in Multi Task Learning: Improving Low Resource Neural Machine Translation
... Neural Machine Translation (NMT) is no- torious for its need for large amounts of bilingual ...is Multi- Task Learning (MTL) to leverage differ- ent linguistic ... See full document
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Improving Robustness of Neural Machine Translation with Multi task Learning
... Real world data, especially in the realm of social media, often contains noise such as mis-spellings, grammar errors, or lexical variations. Even though humans do not have much difficulty in recognizing and translating ... See full document
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Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi Task Learning Approach
... Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation ...monolingual linguistic re- sources in the source side to address ... See full document
10
Using Target side Monolingual Data for Neural Machine Translation through Multi task Learning
... through multi-task ...against using synthetic parallel data, we believe there is value in pursuing this line of re- search further to simplify training ... See full document
6
Exploiting Source side Monolingual Data in Neural Machine Translation
... more translation rules in SMT and we also adapt a multi-task learning framework to take full advantage of the source-side monolingual ...the multi-task learning frame- ... See full document
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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 ...(CLIR) task is ... See full document
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Ensemble Learning for Multi Source Neural Machine Translation
... the linguistic hypothesis that translation systems from different source language into the same target language have complementary strengths and weak- nesses in terms of translation performance and ... See full document
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Exploiting Deep Representations for Neural Machine Translation
... while multi-layer atten- tion decreases decoding speed by 21% due to an additional attention process for each ...improve translation performance, it- erative and hierarchical aggregation strategies achieve ... See full document
10
Exploiting Sentential Context for Neural Machine Translation
... French translation tasks show that exploiting sentential context consistently improves translation perfor- mance across language ...claim. Linguistic analyses (Conneau et ... See full document
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Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
... the linguistic bias to be more valuable in the resource-poor setting, the improve- ment from using semantic-role structures is larger here ...from using treebank syntax, whereas (often less local and ... See full document
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Exploiting Monolingual Data at Scale for Neural Machine Translation
... back translation (BT) approach to leverage the target-side monolingual data, which is simple and ...The translation output and the target-side monolingual data then paired as synthetic paral- lel corpus to ... See full document
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Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation
... empirical study of efficient training on multiple in-domain and out-of-domain datasets. We ap- plied transfer learning by training NMT systems with different datasets one after another carrying on the previous ... See full document
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Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation
... a multi-source NMT approach for the low-resource NMT to explicitly utilize the source-side linguistic knowledge, which models the word sequence in parallel to the linguistic features by using ... See full document
9
Multi agent Learning for Neural Machine Translation
... Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, ...dual learning, and bidirectional decoding with one agent decod- ing from left to ... See full document
10
Linguistic Input Features Improve Neural Machine Translation
... a fixed symbol vocabulary, using a segmentation based on byte-pair encoding (BPE) (Sennrich et al., 2016c). We note that in BPE segmentation, some symbols are potentially ambiguous, and can either be a separate ... See full document
9
Modular Neural Network Approach for Data Classification
... challenging task that has important application in real life and its application are excepted to grow more in ...Modular Neural Network as a modelling tool for data ...novel task decomposition and ... See full document
9
Multi Task Active Learning for Linguistic Annotations
... active learning (AL) approach. In the multi-task ac- tive learning (MTAL) paradigm, we select ex- amples for several annotation tasks rather than for a single one as usually done in the con- ... See full document
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Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... Neural machine translation has significantly pushed forward the quality of the ...ness. Neural models are trained on large text corpora which contain biases and ...in neural ma- chine ... See full document
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