Why We Need New Evaluation Metrics for NLG J Novikova, O Dušek, AC Curry, V Rieser Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 547 | 2017 |
The E2E dataset: New challenges for end-to-end generation J Novikova, O Dušek, V Rieser Proceedings of the 18th Annual Meeting of the Special Interest Group on …, 2017 | 429 | 2017 |
Evaluating the state-of-the-art of end-to-end natural language generation: The E2E NLG Challenge O Dušek, J Novikova, V Rieser Computer Speech & Language 59, 123-156, 2020 | 248 | 2020 |
Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings O Dušek, F Jurčíček 54th Annual Meeting of the Association for Computational Linguistics, 45-51, 2016 | 211 | 2016 |
The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 136 | 2021 |
Findings of the E2E NLG challenge O Dušek, J Novikova, V Rieser Proceedings of the 11th International Conference on Natural Language …, 2018 | 126 | 2018 |
RankME: Reliable Human Ratings for Natural Language Generation J Novikova, O Dušek, V Rieser Proceedings of the 16th Annual Conference of the North American Chapter of …, 2018 | 115 | 2018 |
Semantic Noise Matters for Neural Natural Language Generation O Dušek, DM Howcroft, V Rieser Proceedings of the 12th International Conference on Natural Language …, 2019 | 113 | 2019 |
CzEng 1.6: enlarged Czech-English parallel corpus with processing tools dockered O Bojar, O Dušek, T Kocmi, J Libovický, M Novák, M Popel, R Sudarikov, ... International Conference on Text, Speech, and Dialogue, 231-238, 2016 | 108 | 2016 |
A context-aware natural language generator for dialogue systems O Dušek, F Jurčíček SIGDIAL 2016, 185-190, 2016 | 100 | 2016 |
HamleDT: Harmonized multi-language dependency treebank D Zeman, O Dušek, D Mareček, M Popel, L Ramasamy, J Štěpánek, ... Language Resources and Evaluation 48 (4), 601-637, 2014 | 87 | 2014 |
The Joy of Parallelism with CzEng 1.0 O Bojar, Z Žabokrtský, O Dušek, P Galušcáková, M Majliš, D Marecek, ... LREC, 2012 | 83 | 2012 |
Better conversations by modeling, filtering, and optimizing for coherence and diversity X Xu, O Dušek, I Konstas, V Rieser Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 74 | 2018 |
Evaluating semantic accuracy of data-to-text generation with natural language inference O Dušek, Z Kasner arXiv preprint arXiv:2011.10819, 2020 | 72 | 2020 |
One Model, Many Languages: Meta-learning for Multilingual Text-to-Speech T Nekvinda, O Dušek INTERSPEECH, 2020 | 72 | 2020 |
Nl-augmenter: A framework for task-sensitive natural language augmentation KD Dhole, V Gangal, S Gehrmann, A Gupta, Z Li, S Mahamood, ... arXiv preprint arXiv:2112.02721, 2021 | 71 | 2021 |
Adaptation of machine translation for multilingual information retrieval in the medical domain P Pecina, O Dušek, L Goeuriot, J Hajič, J Hlaváčová, GJF Jones, L Kelly, ... Artificial intelligence in medicine 61 (3), 165-185, 2014 | 67 | 2014 |
DEPFIX: A System for Automatic Correction of Czech MT Outputs R Rosa, D Marecek, O Dušek WMT'12, 2012 | 67 | 2012 |
Are LLMs all you need for task-oriented dialogue? V Hudeček, O Dušek arXiv preprint arXiv:2304.06556, 2023 | 61 | 2023 |
Leak, cheat, repeat: Data contamination and evaluation malpractices in closed-source llms S Balloccu, P Schmidtová, M Lango, O Dušek arXiv preprint arXiv:2402.03927, 2024 | 57 | 2024 |