Extending Neural Generative Conversational Model using External Knowledge Sources
This work addresses the issue of poor content in conversational agents for chit-chat applications, but it is incremental as it builds on existing Seq2Seq models with external knowledge integration.
The authors tackled the problem of generative dialogue models lacking coherence and content by incorporating external knowledge sources like Wikipedia and NELL into a Seq2Seq architecture, resulting in faster training time and improved perplexity.
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.