The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot

By Iulian Serban, Chinnadhurai Sankar, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Sarath Chandar, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brébisson and 5 more. 2017

Subfields: Dialogue & Conversational AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Dialogue & Conversational AI

1. Iulian Serban
2. Ryan J. Lowe
3. Laurent Charlin
4. Joelle Pineau
5. Yoshua Bengio
6. Peter Henderson
7. Behnam Hedayatnia

Citations in the corpus

• A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “…dialogue system architectures based on neural networks have shown promising results on several dialogue tasks (Ritter et al., 2011; Vinyals and Le, 2015; Lowe et al., 2015a; Sordoni et al., 2015b; Shang et al., 2015; Li et al., 2015; Serban et al., 2016; Serban et al., 2017b,a; Dodge et al., 2015).”
• Conversational AI: The Science Behind the Alexa Prize (2018, background): “MILABot, for example, used this approach and trained a reinforcement learning ranker function on conversation ratings.”
• The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach (2018, background): “The dialogue sys-tem is described further in Serban et al. (2017a) (see also Serban et al. (2017b)).”