85 citations · 137 across the 4 of their papers we have counts for
4 papers
Ensemble-Based Deep Reinforcement Learning for Chatbots
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +7
Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing…
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
Heriberto Cuayáhuitl, Donghyeon Lee, Seonghan Ryu +3
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward functi…
A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents
Heriberto Cuayáhuitl, Seonghan Ryu, Donghyeon Lee +1
The amount of dialogue history to include in a conversational agent is often underestimated and/or set in an empirical and thus possibly naive way. This suggests that principled in…
Neural Sentence Embedding using Only In-domain Sentences for Out-of-domain Sentence Detection in Dialog Systems
Seonghan Ryu, Seokhwan Kim, Junhwi Choi +2
To ensure satisfactory user experience, dialog systems must be able to determine whether an input sentence is in-domain (ID) or out-of-domain (OOD). We assume that only ID sentence…