Stephan Gouws

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

2016 · arXiv (Cornell University)

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive …

Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

2017

Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response. Unlike translation, conversation responding is inherently creative. The generation …

Universal Transformers

2018 · arXiv (Cornell University)

Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them …