Machine Translation of Labeled Discourse Connectives

Thomas Meyer, Andrei Popescu-Belis, Najeh Hajlaoui, Andrea Gesmundo


Abstract
This paper shows how the disambiguation of discourse connectives can improve their automatic translation, while preserving the overall performance of statistical MT as measured by BLEU. State-of-the-art automatic classifiers for rhetorical relations are used prior to MT to label discourse connectives that signal those relations. These labels are used for MT in two ways: (1) by augmenting factored translation models; and (2) by using the probability distributions of labels in order to train and tune SMT. The improvement of translation quality is demonstrated using a new semi-automated metric for discourse connectives, on the English/French WMT10 data, while BLEU scores remain comparable to non-discourse-aware systems, due to the low frequency of discourse connectives.
Anthology ID:
2012.amta-papers.20
Volume:
Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
Month:
October 28-November 1
Year:
2012
Address:
San Diego, California, USA
Venue:
AMTA
SIG:
Publisher:
Association for Machine Translation in the Americas
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Language:
URL:
https://aclanthology.org/2012.amta-papers.20
DOI:
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Cite (ACL):
Thomas Meyer, Andrei Popescu-Belis, Najeh Hajlaoui, and Andrea Gesmundo. 2012. Machine Translation of Labeled Discourse Connectives. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.
Cite (Informal):
Machine Translation of Labeled Discourse Connectives (Meyer et al., AMTA 2012)
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PDF:
https://aclanthology.org/2012.amta-papers.20.pdf