@inproceedings{parthasarathi-pineau-2018-extending,
title = "Extending Neural Generative Conversational Model using External Knowledge Sources",
author = "Parthasarathi, Prasanna and
Pineau, Joelle",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1073",
doi = "10.18653/v1/D18-1073",
pages = "690--695",
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.",
}
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%0 Conference Proceedings
%T Extending Neural Generative Conversational Model using External Knowledge Sources
%A Parthasarathi, Prasanna
%A Pineau, Joelle
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F parthasarathi-pineau-2018-extending
%X 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.
%R 10.18653/v1/D18-1073
%U https://aclanthology.org/D18-1073
%U https://doi.org/10.18653/v1/D18-1073
%P 690-695
Markdown (Informal)
[Extending Neural Generative Conversational Model using External Knowledge Sources](https://aclanthology.org/D18-1073) (Parthasarathi & Pineau, EMNLP 2018)
ACL