[PDF] Top 20 Data Augmentation for Low Resource Neural Machine Translation

... Given a source and target sentence pair (S,T), we want to alter it in a way that preserves the semantic equivalence between S and T while diversifying as much as possible the training examples. A number of ways to do ... See full document

... Li et al. (2016) present a dynamic NMT ap- proach where the general NMT model is adapted per-sentence; however, they adapt on only a single similar sentence and employ their system in a high- resource context. We ... See full document

... attention-based neural machine translation model which consists of a one-layer bidirectional RNN encoder and a two-layer attention-based RNN ... See full document

... on low resource NMT has focused on exploiting monolingual data or parallel data from other language ...lingual data ranges from back-translation (Sen- nrich et ...for low ... See full document

... convolutional/recurrent neural net- works might be very different from simple feed- forward ones which might be still an open prob- lem in theoretic deep learning ... See full document

... The baselines are either models trained purely on the child parallel data or only on the parent data. The second baseline only indicates the relat- edness of languages because it is only tested but never ... See full document

... pivot-based machine translation, text is first translated from the source language into the pivot language, and then from the pivot language into the target ...strong translation performance (Johnson ... See full document

... The unsupervised parses are trained over sub- words; if the induced hierarchies have a linguis- tic basis, we would expect the model to combine subwords into words as a first step. For each model, we calculate the ... See full document

... Additionally, the organizers provided monolingual datasets for Spanish, Portuguese, Czech and Polish. They all largely came from the same sources including the Europarl, JRC-Acquis, New Crawl, and News Commentary ... See full document

... In this study, we have proposed three difference strategies to handle rare words in NMT, in which the combination of methods brings significant im- provements to the NMT systems on two low- resource ... See full document

... four low-resource language pairs. Parallel data for Swahili↔English (SW↔EN), Tagalog↔English (TL↔EN) and Somali↔English (SO↔EN) contains a mix- ture of domains such as news and weblogs and is ... See full document

... consider translation as the main task along with syntactic and semantic auxiliary tasks, and re-weight instances in such a way to max- imise the performance of the translation ... See full document

... To increase the overlap between the parent and child vocabularies, we use BPE to break words into subwords. For the BPE merge rules to not only find the common subwords between two source languages but also ensure ... See full document

... ral machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of aux- ... See full document

... on translation time and quality of incremental model ...post-edit translation candidates, translations that improve over time might reduce this post- editing effort and, consequently reduce the over- all ... See full document

... improve low-resource neural machine translation (NMT), the synthetic bilingual cor- pora generated by NMT models trained on limited authentic bilingual data are inevitably ... See full document

... examined data selection from the view of domain adaptation, selecting good train- ing data from out-of-domain text to improve in- domain ...select data that score above a preset threshold ac- cording ... See full document

... When trained on a larger pseudo parallel data, 2-to-2 models achieved a higher accuracy for both coreference and coherence/cohesion datasets. Our 2-to-2 model trained using 2M pseudo parallel data ... See full document

... the neural encoder-decoder framework for MT (Neco and Forcada, 1997; Casta˜no and Casacu- berta, 1997; Sutskever et ...recurrent neural network (encoder) to convert a source sen- tence into a dense, ... See full document

... for low 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 ... See full document