Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung
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 phenomenon 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 integrated with live APIs and show that the dialogue simulator is an essential component of the system that leads to over 50% improvement in turn-level action signature prediction accuracy.
BibTeX
@inproceedings{acharya-etal-2021-alexa,
title = "{A}lexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems",
author = "Acharya, Anish and
Adhikari, Suranjit and
Agarwal, Sanchit and
Auvray, Vincent and
Belgamwar, Nehal and
Biswas, Arijit and
Chandra, Shubhra and
Chung, Tagyoung and
Fazel-Zarandi, Maryam and
Gabriel, Raefer and
Gao, Shuyang and
Goel, Rahul and
Hakkani-Tur, Dilek and
Jezabek, Jan and
Jha, Abhay and
Kao, Jiun-Yu and
Krishnan, Prakash and
Ku, Peter and
Goyal, Anuj and
Lin, Chien-Wei and
Liu, Qing and
Mandal, Arindam and
Metallinou, Angeliki and
Naik, Vishal and
Pan, Yi and
Paul, Shachi and
Perera, Vittorio and
Sethi, Abhishek and
Shen, Minmin and
Strom, Nikko and
Wang, Eddie",
editor = "Sil, Avi and
Lin, Xi Victoria",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-demos.15/",
doi = "10.18653/v1/2021.naacl-demos.15",
pages = "125--132"
}