Link to original paper: https://arxiv.org/pdf/1609.08144v2.pdf
This paper describes an approach to the task of Machine Translation. Authored by Google ML visionaries Jeff Dean, Greg Corrado, Orial Vinyals, and others, this paper introduced a machine translation system that serves as the backbone behind Google’s popular Translate service. The system reduced translation errors by an average of 60% compared to the previous production system Google used.
Traditional approaches to automated translation include variants of phrase-based matching. This approach required large amounts of linguistic domain knowledge and ultimately its design proved to be too brittle and lacked generalization ability. One of the problems with the traditional approach was that it would try to translate the input sentence piece by piece. It turns out the more effective approach (that NMT uses) is to translate the whole sentence at a time, thus allowing for a broader context and a more natural rearrangement of words.
The authors in this paper introduce a deep LSTM network that can be trained end to end with 8 encoder and decoder layers. We can separate the system into 3 components, the encoder RNN, decoder RNN, and attention module. From a high level, the encoder works on the task on transforming the input sentence to vector representation, the decoder produces the output representation, and then the attention module tells the decoder what to focus on during the task of decoding (This is where the idea of utilizing the whole context of the sentence comes in).
The rest of the paper mainly focuses on the challenges associated with deploying such a service at scale. Topics such as amount of computational resources, latency, and high volume deployment are discussed at length.