activity
20162024
most citedPlanning for Goal-Oriented Dialogue Systems

17 citations · 24 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG2024

Toward Information Theoretic Active Inverse Reinforcement Learning

Ondrej Bajgar, Sid William Gould, Rohan Narayan Langford Mitta +3

As AI systems become increasingly autonomous, aligning their decision-making to human preferences is essential. In domains like autonomous driving or robotics, it is impossible to…

cs.AI201917 cited

Planning for Goal-Oriented Dialogue Systems

Christian Muise, Tathagata Chakraborti, Shubham Agarwal +6

Generating complex multi-turn goal-oriented dialogue agents is a difficult problem that has seen a considerable focus from many leaders in the tech industry, including IBM, Google,…

cs.AI20197 cited

Generating Dialogue Agents via Automated Planning

Adi Botea, Christian Muise, Shubham Agarwal +9

Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced…

cs.LG2018

A Boo(n) for Evaluating Architecture Performance

Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst

We point out important problems with the common practice of using the best single model performance for comparing deep learning architectures, and we propose a method that corrects…

cs.LG2017

Knowledge Base Completion: Baselines Strike Back

Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst

Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on…

cs.CL2016

Text Understanding with the Attention Sum Reader Network

Rudolf Kadlec, Martin Schmid, Ondrej Bajgar +1

Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these…