Search Results for author: Jan Kleindienst

Found 10 papers, 3 papers with code

A Boo(n) for Evaluating Architecture Performance

1 code implementation ICML 2018 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 these flaws.

Hybrid Dialog State Tracker with ASR Features

no code implementations EACL 2017 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.

Slot Filling Spoken Language Understanding

Embracing data abundance: BookTest Dataset for Reading Comprehension

2 code implementations4 Oct 2016 Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst

We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture.

Reading Comprehension

Text Understanding with the Attention Sum Reader Network

2 code implementations ACL 2016 Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, Jan Kleindienst

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.

Ranked #5 on Open-Domain Question Answering on SearchQA (Unigram Acc metric)

Machine Reading Comprehension Open-Domain Question Answering

Improved Deep Learning Baselines for Ubuntu Corpus Dialogs

no code implementations13 Oct 2015 Rudolf Kadlec, Martin Schmid, Jan Kleindienst

The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset.

Conversational Response Selection

Hybrid Dialog State Tracker

no code implementations13 Oct 2015 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.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.