no code implementations • 31 Mar 2016 • Miroslav Vodolán, Filip Jurčíček
This paper presents a dataset collected from natural dialogs which enables to test the ability of dialog systems to learn new facts from user utterances throughout the dialog.
1 code implementation • 17 Jun 2016 • Ondřej Dušek, Filip Jurčíček
We present a natural language generator based on the sequence-to-sequence approach that can be trained to produce natural language strings as well as deep syntax dependency trees from input dialogue acts, and we use it to directly compare two-step generation with separate sentence planning and surface realization stages to a joint, one-step approach.
no code implementations • 28 Jun 2016 • Ondřej Plátek, Petr Bělohlávek, Vojtěch Hudeček, Filip Jurčíček
This paper discusses models for dialogue state tracking using recurrent neural networks (RNN).
1 code implementation • 25 Aug 2016 • Ondřej Dušek, Filip Jurčíček
We present a novel natural language generation system for spoken dialogue systems capable of entraining (adapting) to users' way of speaking, providing contextually appropriate responses.
1 code implementation • 9 Jan 2018 • Miroslav Vodolán, Filip Jurčíček
This paper presents a novel task using real user data obtained in human-machine conversation.
2 code implementations • 11 Oct 2019 • Ondřej Dušek, Filip Jurčíček
We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach.