91 citations · 160 across the 5 of their papers we have counts for
7 papers
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…
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…
Hybrid Dialog State Tracker with ASR Features
Miroslav Vodolán, Rudolf Kadlec, Jan Kleindienst
This paper presents a hybrid dialog state tracker enhanced by trainable Spoken Language Understanding (SLU) for slot-filling dialog systems. Our architecture is inspired by previou…
Embracing data abundance: BookTest Dataset for Reading Comprehension
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current com…
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…
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
Rudolf Kadlec, Martin Schmid, Jan Kleindienst
This paper presents results of our experiments for the next utterance ranking on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog corpus. First, we use…
Hybrid Dialog State Tracker
Miroslav Vodolán, Rudolf Kadlec, Jan Kleindienst
This paper presents a hybrid dialog state tracker that combines a rule based and a machine learning based approach to belief state tracking. Therefore, we call it a hybrid tracker.…