Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles

Aitor Álvarez, Marina Balenciaga, Arantza del Pozo, Haritz Arzelus, Anna Matamala, Carlos-D. Martínez-Hinarejos


Abstract
This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.
Anthology ID:
L16-1487
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3049–3053
Language:
URL:
https://aclanthology.org/L16-1487
DOI:
Bibkey:
Cite (ACL):
Aitor Álvarez, Marina Balenciaga, Arantza del Pozo, Haritz Arzelus, Anna Matamala, and Carlos-D. Martínez-Hinarejos. 2016. Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3049–3053, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles (Álvarez et al., LREC 2016)
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PDF:
https://aclanthology.org/L16-1487.pdf