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        <title>Like Chalk and Cheese? On the Effects of Translationese in MT Training</title>
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        <namePart type="given">Samuel</namePart>
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            <title>Proceedings of Machine Translation Summit XVIII: Research Track</title>
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            <namePart type="given">Kevin</namePart>
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    <abstract>We revisit the topic of translation direction in the data used for training neural machine translation systems and focusing on a real-world scenario with known translation direction and imbalances in translation direction: the Canadian Hansard. According to automatic metrics and we observe that using parallel data that was produced in the “matching” translation direction (Authentic source and translationese target) improves translation quality. In cases of data imbalance in terms of translation direction and we find that tagging of translation direction can close the performance gap. We perform a human evaluation that differs slightly from the automatic metrics and but nevertheless confirms that for this French-English dataset that is known to contain high-quality translations and authentic or tagged mixed source improves over translationese source for training.</abstract>
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%0 Conference Proceedings
%T Like Chalk and Cheese? On the Effects of Translationese in MT Training
%A Larkin, Samuel
%A Simard, Michel
%A Knowles, Rebecca
%Y Duh, Kevin
%Y Guzmán, Francisco
%S Proceedings of Machine Translation Summit XVIII: Research Track
%D 2021
%8 August
%I Association for Machine Translation in the Americas
%C Virtual
%F larkin-etal-2021-like
%X We revisit the topic of translation direction in the data used for training neural machine translation systems and focusing on a real-world scenario with known translation direction and imbalances in translation direction: the Canadian Hansard. According to automatic metrics and we observe that using parallel data that was produced in the “matching” translation direction (Authentic source and translationese target) improves translation quality. In cases of data imbalance in terms of translation direction and we find that tagging of translation direction can close the performance gap. We perform a human evaluation that differs slightly from the automatic metrics and but nevertheless confirms that for this French-English dataset that is known to contain high-quality translations and authentic or tagged mixed source improves over translationese source for training.
%U https://aclanthology.org/2021.mtsummit-research.9
%P 103-113

Markdown (Informal)

[Like Chalk and Cheese? On the Effects of Translationese in MT Training](https://aclanthology.org/2021.mtsummit-research.9) (Larkin et al., MTSummit 2021)

• Like Chalk and Cheese? On the Effects of Translationese in MT Training (Larkin et al., MTSummit 2021)

ACL

• Samuel Larkin, Michel Simard, and Rebecca Knowles. 2021. Like Chalk and Cheese? On the Effects of Translationese in MT Training. In Proceedings of Machine Translation Summit XVIII: Research Track, pages 103–113, Virtual. Association for Machine Translation in the Americas.