Seonghan Ryu

A Study on Dialogue Reward Prediction for Open-Ended Conversational Agents

2018 · arXiv (Cornell University)

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 investigations into optimal context windows are urgently …