Automatic Construction of Discourse Corpora for Dialogue Translation

Longyue Wang, Xiaojun Zhang, Zhaopeng Tu, Andy Way, Qun Liu


Abstract
In this paper, a novel approach is proposed to automatically construct parallel discourse corpus for dialogue machine translation. Firstly, the parallel subtitle data and its corresponding monolingual movie script data are crawled and collected from Internet. Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts. We not only evaluate the mapping results, but also integrate speaker information into the translation. Experiments show our proposed method can achieve 81.79% and 98.64% accuracy on speaker and dialogue boundary annotation, and speaker-based language model adaptation can obtain around 0.5 BLEU points improvement in translation qualities. Finally, we publicly release around 100K parallel discourse data with manual speaker and dialogue boundary annotation.
Anthology ID:
L16-1436
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
2748–2754
Language:
URL:
https://aclanthology.org/L16-1436
DOI:
Bibkey:
Cite (ACL):
Longyue Wang, Xiaojun Zhang, Zhaopeng Tu, Andy Way, and Qun Liu. 2016. Automatic Construction of Discourse Corpora for Dialogue Translation. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2748–2754, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Automatic Construction of Discourse Corpora for Dialogue Translation (Wang et al., LREC 2016)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/L16-1436.pdf