@inproceedings{alvarez-etal-2016-impact,
title = "Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles",
author = "{\'A}lvarez, Aitor and
Balenciaga, Marina and
del Pozo, Arantza and
Arzelus, Haritz and
Matamala, Anna and
Mart{\'i}nez-Hinarejos, Carlos-D.",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Goggi, Sara and
Grobelnik, Marko and
Maegaard, Bente and
Mariani, Joseph and
Mazo, Helene and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L16-1487/",
pages = "3049--3053",
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."
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%0 Conference Proceedings
%T Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles
%A Álvarez, Aitor
%A Balenciaga, Marina
%A del Pozo, Arantza
%A Arzelus, Haritz
%A Matamala, Anna
%A Martínez-Hinarejos, Carlos-D.
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Grobelnik, Marko
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Helene
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 May
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F alvarez-etal-2016-impact
%X 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.
%U https://aclanthology.org/L16-1487/
%P 3049-3053
Markdown (Informal)
[Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles](https://aclanthology.org/L16-1487/) (Álvarez et al., LREC 2016)
ACL