Gagan Bansal
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Using Machine Translation to Localize Task Oriented NLG Output
Scott Roy
Cliff Brunk
Kyu-Young Kim
Justin Xu Zhao
Sidharth Mudgal
Chris Varano
CoRR, vol. abs/2107.04512 (2021)
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One of the challenges for a task oriented NLG system like the Google Assistant is to internationalize the output to many languages. This paper explores doing this by applying machine translation to the English output. Using machine translation is very scalable, as it can work with any English output and can handle dynamic text, but it is difficult to meet the required quality bar: machine translation is good, but for a commercial NLG application it often needs to be nearly perfect. Fortunately, in task oriented NLG the quality only needs to reach this bar for the narrow range of sentences that the NLG system can actually produce. We are able to reach this quality using a combination of semantic annotations, fine tuning on in-domain translations, automatic error detection, and sentences from the Web. This paper shares our approach and results, together with a distillation model to serve the NMT models at scale.
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Gmail Smart Compose: Real-Time Assisted Writing
Jackie Tsay
Justin Lu
Shuyuan Zhang
Tim Sohn
Yinan Wang
KDD 2019 (2019)
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In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing. In the design and deployment of such a large-scale and complicated system, we faced several challenges including model selection, performance evaluation, serving and other practical issues. At the core of Smart Compose is a large-scale neural language model. We leveraged state-of-the-art machine learning techniques for language model training which enabled high-quality suggestion prediction, and constructed novel serving infrastructure for high-throughput and real-time inference. Experimental results show the effectiveness of our proposed system design and deployment approach. This system is currently being served in Gmail.
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