REVIEW 2 cited by
A Deep Reinforcement Learning Chatbot (Short Version)
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
Ensemble-Based Deep Reinforcement Learning for Chatbots
A response-selection chatbot trained as an ensemble of 100 deep reinforcement learning agents, each on a cluster of dialogues, outperforms a single DRL agent and a Seq2Seq baseline on Persona-Chat.
-
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
A DQN chatbot that selects among 100 clustered reply types and is rewarded for picking true human responses learns on training dialogues but generalizes poorly to unseen dialogues.
Discussion (0). Continue with ORCID to comment.