[PDF] Top 20 Revisiting Low Resource Neural Machine Translation: A Case Study

... on translation quality is relatively small; Haddow et ...In low-resource settings, large vocabularies re- sult in low-frequency (sub)words being repre- sented as atomic units at training time, ... See full document

... For our experiments, the best performing bi- text only systems produced 2 sets of backtrans- lated text. The first set (which we will refer to as Synth 1) included only parts of all the considered monolingual data for a ... See full document

... of Neural Machine Translation (NMT), as a data-driven approach, mas- sively depends on quantity, quality and relevance of the training ...for low-resource ...bilingual low- ... See full document

... characterizing translation quality with respect to morphological variants at the word level, we also perform a manual analysis of the sentences from each system to inspect improvements that may be due to the ... See full document

... 5.4 Direction Swap in Parent and Child Relaxing the setup in Section 5.1, we now allow a mismatch in translation direction of the parent and child. The parent XX-EN is thus followed by an EN-YY child or vice ... See full document

... for neural machine translation seems to improve translation quality in terms of BLEU while also significantly decreasing the number of parameters in the ... See full document

... into neural machine translation (NMT) has recently proven success- ful (Eriguchi et ...for neural machine translation; this model is able to induce an unsupervised hierarchical ... See full document

... convolutional neural network to rec- ognize handwritten characters and show positive ef- fects of transfer between models for Latin and Chi- nese ...first study to apply trans- fer learning to neural ... See full document

... In our previous experiments for English→Vietnamese, BPE algorithm (Sen- nrich et al., 2016b) applied to the source side does not significantly improves the systems despite it is able to reduce the number of unknown ... See full document

... We study the problem of incremental domain adaptation of a generic neural machine trans- lation model with limited resources ...chine translation experiments (Ar-to-En direc- tion) for ... See full document

... Subsequent experiments revealed various fac- tors that influenced the effectiveness of the copied monolingual corpus. An unexpected finding was that doubling and tripling the size of the mono- lingual corpus (whether ... See full document

... improving low-resource NMT with auxiliary linguistic ...the case of low-resource NMT, it has been shown that dynamically changing the weights throughout the training is essential to ... See full document

... Sample Translations We present some sample translations from the tested models in Table 3. Inspecting these examples provides the insight into the proposed meta-learning algorithm. For in- stance, we observe that the ... See full document

... on low resource NMT has focused on exploiting monolingual data or parallel data from other language ...for low resource NMT which exploits parallel data from other language pairs (Zoph et ... See full document

... random translation selection when β = ...random translation selec- tion introduces lower quality samples and there- fore noisier training ...in low-resource set- tings (Edunov et ... See full document

... A common strategy to improve learning of low- resource languages is to use resources from re- lated languages (Nakov and Ng, 2009). However, adapting these resources is not trivial. NMT of- fers some simple ... See full document

... transfer learning (Zoph et al., 2016; Kocmi and Bojar, 2018), and multilingual modeling (Firat et al., 2016). Recently, a simple multilingual modeling (MultiNMT) was proposed by Johnson et al. (2017) which translates ... See full document

... Prior work has examined data selection from the view of domain adaptation, selecting good train- ing data from out-of-domain text to improve in- domain performance. In general, these methods select data that score above ... See full document

... In this work, we propose a method to quantify the confidence of NMT model predictions to en- able back-translation to better cope with transla- tion errors. The central idea is to use model un- certainty (Buntine ... See full document

... English translation into a target language (Fr), all within the same multi-way, mul- tilingual model trained by using bilingual ...One-to-One Translation The most basic ap- proach here is to perform each ... See full document