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Evaluating End-to-End Goal Oriented Dialog Systems
Antoine Bordes

Sat Dec 10 06:50 AM -- 07:30 AM (PST) @ None

Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End- to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging successes recently obtained in chit-chat dialog may not carry over to goal-oriented settings. In this talk, we will discuss how to evaluate end-to-end goal oriented dialog systems in a robust and reproducible manner. We will also present a new testbed designed to that end. On this new dataset, we show that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations. We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (Henderson et al., 2014a) and show similar result patterns on data extracted from an online concierge service.

Author Information

Antoine Bordes (Meta AI)

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