[PDF] Top 20 Overcoming the Rare Word Problem for low resource language pairs in Neural Machine Translation
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Overcoming the Rare Word Problem for low resource language pairs in Neural Machine Translation
... for rare words in the same way as in SAA ...each word in the WordNet can belong to many synsets with different meanings, thus an inappropriate word can be placed in the current source ... See full document
8
Meta Learning for Low Resource Neural Machine Translation
... for low- resource neural machine translation ...frame low-resource translation as a meta- learning problem, and we learn to adapt to ... See full document
10
Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation
... Neural Machine Translation (NMT) is the current state-of- the-art machine translation architecture that aims at building a single neural network that can be jointly tuned to ... See full document
5
Transfer Learning across Low Resource, Related Languages for Neural Machine Translation
... The BPE-based systems could make use of big- ger vocabulary size thanks to the combination of both parent and child source and target vocabu- laries. We varied the number of BPE merge op- erations from 5,000 to 60,000. ... See full document
6
Addressing word order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages
... for Neural Ma- chine Translation (NMT) trains a NMT model on an assisting language-target language pair (parent model) which is later fine-tuned for the source language-target ... See full document
6
Transfer Learning for Low Resource Neural Machine Translation
... prove machine translation ...parent language pair affects performance, and provide an empirical upper bound on transfer performance using an arti- ficial ...English-only language models, copy ... See full document
8
Morphological Word Embeddings for Arabic Neural Machine Translation in Low Resource Settings
... Neural machine translation has achieved im- pressive results in the last few years, but its success has been limited to settings with large amounts of parallel ...a word-based NMT model with ... See full document
11
Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation
... source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language ...Turkish→English machine translation task ... See full document
9
Sentence Level Adaptation for Low Resource Neural Machine Translation
... separate language models and alignment models like statistical machine translation (SMT) (Koehn et ...most language pairs, however, is a ubiquitous ...of resource-rich lan- ... See full document
9
Naive Regularizers for Low-Resource Neural Machine Translation
... Neural machine translation models have little inductive bias, which can be a disad- vantage in low-resource ...and word frequencies, to pe- nalize translations that are very ... See full document
10
Multimodal Neural Machine Translation for Low resource Language Pairs using Synthetic Data
... NMT: two little kids are on the sand. MNMT: two small children are on sand. For this particular example, the overall mean- ing of the source description has been cor- rectly preserved into the target side descrip- tion ... See full document
10
Data Augmentation for Low Resource Neural Machine Translation
... target language data with Byte- pair encoding (BPE) (Sennrich et ...the rare word threshold R to 100, top K words to 1000 and max- imum number N of augmentations per rare word to ...the ... See full document
7
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
... the word nodes output by the leaf ...the word nodes are created using a sequen- tial LSTM (the only difference would be the use of the root node to initialize the ... See full document
7
Neural Machine Translation of Low Resource and Similar Languages with Backtranslation
... For all of our experiments, we use OpenNMT-py (Klein et al., 2017) to handle training and build our models. For our LSTM+Attn model, we used the default parameters provided in the OpenNMT- py toolkit. For the ... See full document
12
Revisiting Low Resource Neural Machine Translation: A Case Study
... While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field (Sutskever et ...chine translation (PBSMT) or ... See full document
11
Trivial Transfer Learning for Low Resource Neural Machine Translation
... Neural machine translation (NMT) has made a big leap in performance and became the unquestion- able winning approach in the past few years (Bah- danau et ...chine translation (Koehn and ... See full document
9
Addressing the Rare Word Problem in Neural Machine Translation
... of rare words on translation quality, we follow Sutskever et ...of rare words and evaluate each group ...and neural MT systems – we use the ensemble systems described in (Sutskever et ...4. ... See full document
9
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... We employ subword units (Sennrich et al., 2016a) to tackle the morphological richness prob- lem. There are two advantages of using the subword-level. First, we can alleviate the OOV is- sue by zeroing out the number of ... See full document
8
A Direct Syntax Driven Reordering Model for Phrase Based Machine Translation
... We present a probabilistic reordering model that models directly the source translation se- quence and explicitly assigns probabilities to the reorderings of the source input with no restrictions on gap, length or ... See full document
9
Neural Machine Translation with Word Predictions
... learning machine translation from the source language to the target ...the problem is caused by the long error back- propagation pipeline of the recurrent structures in multiple time steps, ... See full document
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