metadata

license: cc
configs:
  - config_name: default
    data_files:
      - split: phase_1
        path: phase_1.json
      - split: phase_2
        path: phase_2.json
task_categories:
  - translation
language:
  - en
  - cs
tags:
  - post editing
  - quality
size_categories:
  - 1K<n<10K

Neural Machine Translation Quality and Post-Editing Performance

This is a repository for an experiment relating NMT quality and post-editing efforts, presented at EMNLP2021 (presentation recording). Please cite the following paper when you use this research:

@inproceedings{zouhar2021neural,
  title={Neural Machine Translation Quality and Post-Editing Performance},
  author={Zouhar, Vil{\'e}m and Popel, Martin and Bojar, Ond{\v{r}}ej and Tamchyna, Ale{\v{s}}},
  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  pages={10204--10214},
  year={2021},
  url={https://aclanthology.org/2021.emnlp-main.801/}
}

You can access the data on huggingface:

from datasets import load_dataset
data_p1 = load_dataset("zouharvi/nmt-pe-effects", "phase_1")
data_p2 = load_dataset("zouharvi/nmt-pe-effects", "phase_2")

The first phase is the main one where we can see the effect of NMT quality on post-editing time. The second phase is to estimate the quality of the first post-editing round.

The code is also public.