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17
22

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems

19 April 2021
Anish Acharya
Suranjit Adhikari
Sanchit Agarwal
Vincent Auvray
Nehal Belgamwar
Arijit Biswas
Shubhra Chandra
Tagyoung Chung
Maryam Fazel-Zarandi
Raefer Gabriel
Shuyang Gao
Rahul Goel
Dilek Z. Hakkani-Tür
Jan Jezabek
Abhay Jha
Jiun-Yu Kao
Prakash Krishnan
Peter Ku
Anuj Kumar Goyal
Chien-Wei Lin
Qing Liu
Arindam Mandal
A. Metallinou
V. Naik
Yi Pan
Shachi Paul
Vittorio Perera
Abhishek Sethi
Minmin Shen
N. Strom
Eddie Wang
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Abstract

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Training each component requires annotations which are hard to obtain for every new domain, limiting scalability of such systems. Similarly, rule-based dialogue systems require extensive writing and maintenance of rules and do not scale either. End-to-End dialogue systems, on the other hand, do not require module-specific annotations but need a large amount of data for training. To overcome these problems, in this demo, we present Alexa Conversations, a new approach for building goal-oriented dialogue systems that is scalable, extensible as well as data efficient. The components of this system are trained in a data-driven manner, but instead of collecting annotated conversations for training, we generate them using a novel dialogue simulator based on a few seed dialogues and specifications of APIs and entities provided by the developer. Our approach provides out-of-the-box support for natural conversational phenomena like entity sharing across turns or users changing their mind during conversation without requiring developers to provide any such dialogue flows. We exemplify our approach using a simple pizza ordering task and showcase its value in reducing the developer burden for creating a robust experience. Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over 50%50\%50% improvement in turn-level action signature prediction accuracy.

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