DialogActs based Search and Retrieval for Response Generation in Conversation Systems

Nidhi Arora, Rashmi Prasad, Srinivas Bangalore

Abstract
Designing robust conversation systems with great customer experience requires a team of design experts to think of all probable ways a customer can interact with the system and then author responses for each use case individually. The responses are authored from scratch for each new client and application even though similar responses have been created in the past. This happens largely because the responses are encoded using domain specific set of intents and entities. In this paper, we present preliminary work to define a dialog act schema to merge and map responses from different domains and applications using a consistent domain-independent representation. These representations are stored and maintained using an Elasticsearch system to facilitate generation of responses through a search and retrieval process. We experimented generating different surface realizations for a response given a desired information state of the dialog.
Anthology ID:
2021.icon-main.69
Volume:
Proceedings of the 18th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2021
Address:
National Institute of Technology Silchar, Silchar, India
Editors:
Sivaji Bandyopadhyay, Sobha Lalitha Devi, Pushpak Bhattacharyya
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
564–572
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2021.icon-main.69/
DOI:
Bibkey:
Cite (ACL):
Nidhi Arora, Rashmi Prasad, and Srinivas Bangalore. 2021. DialogActs based Search and Retrieval for Response Generation in Conversation Systems. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 564–572, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).
Cite (Informal):
DialogActs based Search and Retrieval for Response Generation in Conversation Systems (Arora et al., ICON 2021)
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https://preview.aclanthology.org/build-pipeline-with-new-library/2021.icon-main.69.pdf
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