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In this talk, I’ll discuss what happens when you ask an Alexa device to play music. I’ll focus on the Natural Language Understanding (NLU) component, which deals with categorizing and labeling transcribed requests. In particular, I’ll discuss two projects I’ve worked on designed to improve upon the initial labeling. The first uses a BERT-based language model to “correct” requests that appear to be mislabeled. The second is an online learning model that selects from different NLU interpretations using implicit customer feedback. I’ll conclude the talk with a few tips for the industry job search.

Links: Amazon Jobs Page; Amazon Science Page

Papers:

Personalizing natural-language understanding using multi-armed bandits and implicit feedback – Moerchen et al (2020)

Counterfactual Risk Minimization: Learning from Logged Bandit Feedback - Swaminathan & Joachims (2015)

Analysis of Thompson Sampling for the Multi-Armed Bandit Problem – Agrawal et al (2012)

zoom info available https://sites.google.com/view/maddd After the talk, we will do virtual tea/coffee get-together at https://gather.town/KOoFj0aKT5GkEj40/Alder-Room