Speed and scaling

NMT Issue 36 Average Attention Network for Neural Machine Translation

Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic

In Issue#32, we covered the Transformer model for neural machine translation which is the state of the art in neural MT. In this post we explore a technique presented by Zhang et. al. 2018, which modifies the transformer model and speeds up the translation process by 4-7 times across a range of different engines.....

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Author: Raj Nath Patel, Machine Translation Scientist @ Iconic

For all the benefits Neural MT has brought in terms of translation quality, producing output quickly and efficiently is still a challenge for developers. All things being equal, Neural MT is slower than its statistical counterpart. This is particularly the case when running translation on standard processors (CPUs) as opposed to faster, more powerful (but also more...

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Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic

Training a neural machine translation engine is a time consuming task. It typically takes a number of days or even weeks, when running powerful GPUs. Reducing this time is a priority of any neural MT developer. In this post we explore a recent work (Ott et al, 2018), whereby, without compromising the translation quality, they...

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