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        <title>Machine Translation of Labeled Discourse Connectives</title>
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            <title>Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers</title>
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            <publisher>Association for Machine Translation in the Americas</publisher>
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    <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.</abstract>
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%0 Conference Proceedings
%T Machine Translation of Labeled Discourse Connectives
%A Meyer, Thomas
%A Popescu-Belis, Andrei
%A Hajlaoui, Najeh
%A Gesmundo, Andrea
%S Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2012
%8 oct 28 nov 1
%I Association for Machine Translation in the Americas
%C San Diego, California, USA
%F meyer-etal-2012-machine
%X 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.
%U https://aclanthology.org/2012.amta-papers.20

Markdown (Informal)

[Machine Translation of Labeled Discourse Connectives](https://aclanthology.org/2012.amta-papers.20) (Meyer et al., AMTA 2012)

• Machine Translation of Labeled Discourse Connectives (Meyer et al., AMTA 2012)

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.