Exploring the Context of Recurrent Neural Network based Conversational Agents

Raffaele Piccini.*, Gerasimos Spanakis.

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Conversational agents have begun to rise both in the academic (in terms of research) and commercial (in terms of applications) world. This paper investigates the task of building a non-goal driven conversational agent, using neural network generative models and analyzes how the conversation context is handled. It compares a simpler Encoder-Decoder with a Hierarchical Recurrent Encoder-Decoder architecture, which includes an additional module to model the context of the conversation using previous utterances information. We found that the hierarchical model was able to extract relevant context information and include them in the generation of the output. However, it performed worse (35-40%) than the simple Encoder-Decoder model regarding both grammatically correct output and meaningful response. Despite these results, experiments demonstrate how conversations about similar topics appear close to each other in the context space due to the increased frequency of specific topic-related words, thus leaving promising directions for future research and how the context of a conversation can be exploited.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
EditorsAP Rocha, L Steels, J VanDenHerik
Number of pages10
ISBN (Print)978-989-758-350-6
Publication statusPublished - Feb 2019


  • Conversational Agents
  • Hierarchical Recurrent Encoder Decoder
  • Recurrent Neural Networks

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