Extending Neural Generative Conversational Model using External Knowledge Sources

Prasanna Parthasarathi, Joelle Pineau


Abstract
The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.
Anthology ID:
D18-1073
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
690–695
Language:
URL:
https://aclanthology.org/D18-1073
DOI:
10.18653/v1/D18-1073
Bibkey:
Cite (ACL):
Prasanna Parthasarathi and Joelle Pineau. 2018. Extending Neural Generative Conversational Model using External Knowledge Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 690–695, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Extending Neural Generative Conversational Model using External Knowledge Sources (Parthasarathi & Pineau, EMNLP 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/D18-1073.pdf
Data
NELL