We study conversational domain exploration (CODEX), where the user's goal is to enrich her knowledge of a given domain by conversing with an informative bot. Such conversations should be well grounded in high-quality domain knowledge as well as engaging and open-ended. A CODEX bot should be proactive and introduce relevant information even if not directly asked for by the user. The bot should also appropriately pivot the conversation to undiscovered regions of the domain. To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses. This allows the bot to control the source, correctness and quality of the offered content, while achieving flexibility via a dialogue manager that selects the most appropriate contents in a compositional manner. We implemented a CODEX bot based on dynamic composition and integrated it into the Google Assistant . As an example domain, the bot conversed about the NBA basketball league in a seamless experience, such that users were not aware whether they were conversing with the vanilla system or the one augmented with our CODEX bot. Results are positive and offer insights into what makes for a good conversation. To the best of our knowledge, this is the first real user experiment of open-ended dialogues as part of a commercial assistant system.
|Title of host publication||The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||12|
|State||Published - 20 Apr 2020|
|Event||29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China|
Duration: 20 Apr 2020 → 24 Apr 2020
|Name||The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020|
|Conference||29th International World Wide Web Conference, WWW 2020|
|Country/Territory||Taiwan, Province of China|
|Period||20/04/20 → 24/04/20|
Bibliographical notePublisher Copyright:
© 2020 ACM.
- Conversational system
- open-ended dialogue
- personal assistant