2018/01/20 by Iulian Vlad Serban, Iulian V. Serban, Serban, Iulian V. +37 · 1 voice · 14 citations
Computer Science · Decision Sciences · Mathematics · Psychology · #Advanced Bandit Algorithms Research #Artificial intelligence #Chatbot #Computer science #Data Stream Mining Techniques #Natural language processing #Psychology #Reinforcement #Reinforcement Learning in Robotics #Reinforcement learning #Social psychology #cs.AI #cs.CL #cs.LG #cs.NE #stat.ML
paper · pdf · doi:10.48550/arxiv.1801.06700
published in arXiv (Cornell University) (Cornell University) · 9 pages, 1 figure, 2 tables; presented at NIPS 2017, Conversational AI: "Today's Practice and Tomorrow's Potential" Workshop
arxiv created 2018/01/20 · openalex publication_date 2018/01/20 · arxiv updated 2018/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including neural network and template-based models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than other systems. The results highlight the potential of coupling ensemble systems with deep reinforcement learning as a fruitful path for developing real-world, open-domain conversational agents.