[PDF] Top 20 Improving Robustness of Neural Machine Translation with Multi task Learning
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Improving Robustness of Neural Machine Translation with Multi task Learning
... Denoising text: Sakaguchi et al. (2017) pro- poses semi-character level recurrent neural net- work (scRNN) to correct words with scrambling characters. Each word is represented as a vector with elements ... See full document
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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 resources as a ... 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 ... See full document
7
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 ...a multi-task learn- ing ...the machine ... See full document
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Using Target side Monolingual Data for Neural Machine Translation through Multi task Learning
... Further, we train models with synthetic parallel data generated through back-translation (Sennrich et al., 2016). For this, we first train a baseline model in the reverse direction and then translate a random ... See full document
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Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... many neural MT sys- tems is that they do not translate parts of the source sentence, or that parts of the source sentence are translated ...the multi-task system is improved com- pared to the ... See full document
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Improving Robustness in Real World Neural Machine Translation Engines
... Subword translation is an approach used in NMT to tackle out-of-vocabulary (OOV) problem using byte-pair encoding (BPE) or other similar segmentation techniques. It is now defacto to use subwords in NMT as with ... 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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Multi Task Learning for Multiple Language Translation
... recurrent neural network based encoder-decoder framework. We train a unified neural machine translation model under the multi- task learning framework where the encoder is ... 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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Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation
... improve neural machine translation mod- els ...WMT19 Robustness Task for the Fr↔En language ...ward translation and fuzzy matches as alternatives to back translation to ... See full document
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Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning
... on translation re- ranking task on the baseline Transformer (Vaswani et ...Zh-En translation. More- over, in order to obtain more translation candi- dates, we expand the beam size to 24 and ... See full document
11
Ensemble Learning for Multi Source Neural Machine Translation
... a multi-source ensemble (Firat et ...training task to an NMT learning algorithm. Multi-source ensembles offer a linguistic source of variation for translation systems, which may range ... See full document
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Improving Anaphora Resolution in Neural Machine Translation Using Curriculum Learning
... self-attention over the target and multi-head atten- tion (MHA) over the encoded main sentence rep- resentation. We further introduce a MHA sublayer over the context representation. The output of the main sentence ... See full document
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Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation
... (Luong and Manning, 2015) adapts an already existing NMT system to a new domain by further training on the in-domain data only. (Freitag and Al-Onaizan, 2016) in addition use checkpoint en- sembling (Sennrich et al., ... See full document
7
Findings of the First Shared Task on Machine Translation Robustness
... encourage robustness (detailed ablations on the effect of each method were not ...adjusting learning rate, applying better regularization and other complicated ... See full document
12
Improving Robustness of Machine Translation with Synthetic Noise
... assist neural models in differ- entiating ...nuanced task and treating the problem as a simple domain adaptation task may fail to fully account for the varied types of noise that can occur in in- ... See full document
5
A Shared Task on Bandit Learning for Machine Translation
... at improving customer experience on the Amazon retail website (improving read- ability, correction of typos, rewriting of uncom- mon abbreviations, removing irrelevant informa- tion, ...and learning ... See full document
11
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
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Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... Human-Informed Translation and Interpreting Technology (HiT-IT 2019) took place in Varna, Bulgaria and spanned over two days (5-6 September 2019), as a post-RANLP 2019 conference ... See full document
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