The Unreasonable Effectiveness Of Neural Machine Translation: A Breakthrough In Temporal Expression Understanding

Written by Rakesh Chada and Marcos Jimenez, data scientists at x.ai. At x.ai we strive to make pain associated with scheduling meetings a thing of the past. We've built a virtual assistant (it goes by the name of Amy or Andrew) who can be cc'd into your typical request to meet with people over email. Amy will "understand" the hand-over and just take it from there with your guests, following up with them to nail the time and location details for the meeting. Under the hood this means that Amy must automatically extract meeting-related pieces of information from your email and, mashing that up with your calendar and overall preferences, proceed to get your guests to agree to a time that works for you and them, plus gather whatever other details are needed for the meeting (phone conference number, meeting room, address, google hangout link, etc …). Now the hard, cool, data-science part. Amy "understanding" all the pieces of information from free-form human text presents us with a number of formidable and fascinating data science challenges. This is the realm of natural language processing (NLP), where recent strides in deep learning have made tackling these problems viable. The problem goes far beyond simply detecting words related to times and locations, or named entity recognition (NER).