Natural Language Generation at Scale: A Case Study for Open Domain Question Answering

Alessandra Cervone, Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Anu Venkatesh, Dilek Hakkani-Tur, Raefer Gabriel


Abstract
Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of data for each domain, and do not benefit from examples that may be available for other domains. This work explores the feasibility of applying statistical NLG to scenarios requiring larger ontologies, such as multi-domain dialog applications or open-domain question answering (QA) based on knowledge graphs. We model NLG through an Encoder-Decoder framework using a large dataset of interactions between real-world users and a conversational agent for open-domain QA. First, we investigate the impact of increasing the number of slot types on the generation quality and experiment with different partitions of the QA data with progressively larger ontologies (up to 369 slot types). Second, we perform multi-task learning experiments between open-domain QA and task-oriented dialog, and benchmark our model on a popular NLG dataset. Moreover, we experiment with using the conversational context as an additional input to improve response generation quality. Our experiments show the feasibility of learning statistical NLG models for open-domain QA with larger ontologies.
Anthology ID:
W19-8657
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
453–462
Language:
URL:
https://aclanthology.org/W19-8657
DOI:
10.18653/v1/W19-8657
Bibkey:
Cite (ACL):
Alessandra Cervone, Chandra Khatri, Rahul Goel, Behnam Hedayatnia, Anu Venkatesh, Dilek Hakkani-Tur, and Raefer Gabriel. 2019. Natural Language Generation at Scale: A Case Study for Open Domain Question Answering. In Proceedings of the 12th International Conference on Natural Language Generation, pages 453–462, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Natural Language Generation at Scale: A Case Study for Open Domain Question Answering (Cervone et al., 2019)
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PDF:
https://aclanthology.org/W19-8657.pdf