[PDF] Top 20 Universal Neural Machine Translation for Extremely Low Resource Languages

... been discussed; without trainable A the model con- fuses "india" and "china" as they may have close representation in the mono-lingual embeddings. Visualization of MoLE Figure 6 shows the ac- tivations ... See full document

... Neural machine translation has achieved state- of-the-art performance for various language pairs (Luong et ...for low-resource languages such as Slovene or Vietnamese, and in ... See full document

... Munteanu and Marcu (2005) proposed a paral- lel sentence extraction system which used com- parable corpora from newspaper articles to ex- tract the parallel sentence pairs. In this procedure, a maximum entropy classifier ... 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

... rich-resource languages but low-resource languages do not have the luxury of such ...such languages, rule-based transliteration is the only viable ...into languages with a ... See full document

... the low-resource experiments, and Firat et ...both low-resource and high-resource lan- guages, while in our case the datasets come from vastly different domains, which makes the task ... 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

... Machine translation, which aims to perform transition between distinct languages, is a major focus of NLP research ...improve machine translation ...factored translation models ... See full document

... a low score to unusual let- ter sequences like “aeus” in Bartimaeus or “phry” in Phry- ...OpenNMT. Neural machine translation is state-of-the-art for many language pairs (Bojar et ...the ... See full document

... of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is consid- ered the most severe one, especially in trans- lation of low-resource ...in ... See full document

... three low-resource languages, Somali, Swahili, and Tagalog, using neural machine trans- ...using neural machine ... See full document

... in low-data settings, and can outper- form PBSMT with far less parallel training data than previously ...in low-resource MT research has been the bet- ter exploitation of monolingual and multilingual ... See full document

... into neural machine translation (NMT) has recently proven success- ful (Eriguchi et ...for languages or domains for which a reliable parser is not ...for neural machine ... See full document

... The differing tokens are more interesting: “-” denotes the cases when the improved system pro- duced something different from the baseline and also from the reference. Gains in BLEU are due to “r” tokens, i.e. tokens ... See full document

... Choi, Gyu Hyeon, Jong Hun Shin, and Young Kil Kim. 2018. Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages. In chair), ... See full document

... We found that too much of the sampled back- translated text did not necessarily improve trans- lation quality. Between the Synth 1 and Synth 2 synthetic sets, we can see a small drop of per- formance particularly for ... See full document

... a low-resource ...a low-resource language pair by exploiting its lexical similarity with another related, low-resource ...agglutinative languages; further investigation ... 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

... for multilingual NMT. However, some work ex- ists for other NLP tasks in a multilingual setting. For Named Entity Recognition (NER), Xie et al. (2018) use a self-attention layer after the Bi-LSTM layer to address ... See full document

... with low-resource parallel ...multi-source translation approach where the model has multiple encoders and attention mechanisms for each source language (Dabre et ...Multi-source translation is ... See full document