activity
20172022
most citedEvaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge

185 citations · 429 across the 14 of their papers we have counts for

collaborators

29 papers

cs.CL20221 cited

Learning Interpretable Latent Dialogue Actions With Less Supervision

Vojtěch Hudeček, Ondřej Dušek

We present a novel architecture for explainable modeling of task-oriented dialogues with discrete latent variables to represent dialogue actions. Our model is based on variational…

cs.CL2022

AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog

Tomáš Nekvinda, Ondřej Dušek

We introduce AARGH, an end-to-end task-oriented dialog system combining retrieval and generative approaches in a single model, aiming at improving dialog management and lexical div…

cs.CL2022

Neural Pipeline for Zero-Shot Data-to-Text Generation

Zdeněk Kasner, Ondřej Dušek

In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data representation and repeating training data noise. We examine how to avoid finetuning p…

cs.CL2021

MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization

Xinnuo Xu, Ondřej Dušek, Shashi Narayan +2

One of the most challenging aspects of current single-document news summarization is that the summary often contains 'extrinsic hallucinations', i.e., facts that are not present in…

cs.CL20211 cited

Underreporting of errors in NLG output, and what to do about it

Emiel van Miltenburg, Miruna-Adriana Clinciu, Ondřej Dušek +8

We observe a severe under-reporting of the different kinds of errors that Natural Language Generation systems make. This is a problem, because mistakes are an important indicator o…

cs.CL2021

AGGGEN: Ordering and Aggregating while Generating

Xinnuo Xu, Ondřej Dušek, Verena Rieser +1

We present AGGGEN (pronounced 'again'), a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input ag…