Customer-obsessed science

Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency. However, such descriptions may not always be available or may pose privacy concerns. To tackle this bottleneck, we introduce PersonaPKT, a lightweight transfer learning approach that can build persona-consistent dialogue models without explicit

The expectations of e-commerce customers include the ability to shop online in their preferred language. Modern e-commerce platforms utilize machine translation to provide multilingual product information at scale. However, maintaining machine translation quality that keeps up with an ever-expanding product information remains an open challenge for industrial machine translation systems. Topical clustering

Pool-based active learning techniques have had success producing multi-class classifiers that achieve high accuracy with fewer labels compared to random labeling. However, in an industrial setting where we often have class-level business targets to achieve (e.g., 95% recall at 95% precision for each class), active learning techniques continue to acquire labels for classes that have already met their targets

Companies offering web services routinely run randomized online experiments to estimate the “causal impact” associated with the adoption of new features and policies on key performance metrics of interest. These experiments are used to estimate a variety of effects: the increase in click rate due to the repositioning of a banner, the impact on subscription rate as a consequence of a discount or special

We present a novel strategy to generate learned learning rate schedules for any optimizer using reinforcement learning (RL). Our approach trains a Proximal Policy Optimization (PPO) agent to predict optimal learning rate schedules for SGD, which we compare with other optimizer-scheduler combinations and full grid search. Our experiments show that the agent learns to generate dynamic schedules that result