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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/L16-1487.pdf