[PDF] Top 20 Multilingual Neural Machine Translation with Language Clustering

... for language clus- tering. The first one is clustering based on prior knowledge, where we use the knowledge of lan- guage family to cluster ...uses language embeddings for similarity measurement and ... See full document

... We gratefully acknowledge support from a gift from Bloomberg L.P. and from the Defense Advanced Research Projects Agency (DARPA) Broad Operational Language Translation (BOLT) program under contract ... See full document

... In this paper, we propose novel convolu- tional architectures to dynamically encode the relevant information in the source language. Our model covers the entire source sentence, but can effectively find and ... See full document

... Kikamba language has few language technology tools since the more efficient and popular data driven approaches for developing them suffer from data sparseness due to lack of digitized ...Kikamba ... See full document

... integrating neural language models in translation ...Scaling neural lan- guage models is a difficult task, but crucial for real-world ...between neural models and back-off n- gram ... See full document

... uses language model and word trans- lation scores, with weights optimized to separate clean and synthetic noise ...both language pairs Sinhala–English and ...(probabilistic translation dic- tionaries ... See full document

... The performance of these vanilla seq2seq mod- els substantially degrades with the sequence length since it is difficult to encode long sequences into a single, fixed-size representation. A plausi- ble solution is the ... See full document

... This paper proposes a proof of concept of a new multilingual NMT approach. The current ap- proach is based on joint training without parame- ter or vocabulary sharing by enforcing a compati- ble representation ... See full document

... entire model. The approach of shared vocabulary across multiple languages resulted in a shared em- bedding space. Although the results were promis- ing, the result of the experiments was reported in highly resourced ... See full document

... see whether the domain-adapted system generates more formal translation based on human evalua- tion. The evaluation is inspired by human assess- ment of Niu et al. (2017). We randomly select 50 translation ... See full document

... Specifically, an NMT system first reads the input sentence using an encoder to build a “thought” vector , a sequence or series of numbers that represents the sentence meaning; a decoder, then, processes and provides the ... See full document

... Artetxe et al. (2017) goes further and use adversarial loss to train their translation system. They build a single shared encoder and a single shared decoder, using both denoising autoen- coder loss and ... See full document

... zero-shot translation directions ...zero-shot language pairs quite well and hence enables us to easily improve their quality with a small amount of additional parallel ... See full document

... unsupervised machine translation track of the WMT’19 news shared task from German to ...tistical machine translation (PBSMT) model and a pre-trained language model to combine word-level ... See full document

... We observed the larger improvements when trans- lating to English. We conjecture that this is due to a higher availability of English in most parallel corpora, leading to a better parameter estimation of the English ... See full document

... Table 5 describes the results when translating into English. First, we can see that both multilingual models perform better than the baselines in terms of average BLEU. This shows that massively mul- tilingual ... See full document

... cases, machine translation can be a useful tool for the quick ex- pansion to new languages by producing candidate translation (Dutta Chowdhury et ...Statistical Machine Translation ... See full document

... Another approach is using restricted Boltzmann machines (RBMs) (Niehues and Waibel, 2012) instead of using multi-layer neural networks (Bengio et al., 2003; Schwenk, 2007; Le et al., 2011). Since probability in a ... See full document

... for Machine Translation for low resource ...gual Machine Translation are two important areas of research that tackles this ...how Multilingual MT models are more effective than the ... See full document

... Multilingual NMT has led to impressive gains in translation accuracy of low-resource lan- guages (LRL) (Zoph et al., 2016; Firat et al., 2016; Gu et al., 2018; Neubig and Hu, 2018; Nguyen and Chiang, 2018). ... See full document