Marianna Martindale

Also published as: Marianna J. Martindale


Responsible ‘Gist’ MT Use in the Age of Neural MT
Marianna J. Martindale
Workshop on the Impact of Machine Translation (iMpacT 2020)


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An Exploration of Placeholding in Neural Machine Translation
Matt Post | Shuoyang Ding | Marianna Martindale | Winston Wu
Proceedings of Machine Translation Summit XVII Volume 1: Research Track

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Identifying Fluently Inadequate Output in Neural and Statistical Machine Translation
Marianna Martindale | Marine Carpuat | Kevin Duh | Paul McNamee
Proceedings of Machine Translation Summit XVII Volume 1: Research Track


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Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT
Marianna Martindale | Marine Carpuat
Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)


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A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
Xing Niu | Marianna Martindale | Marine Carpuat
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Stylistic variations of language, such as formality, carry speakers’ intention beyond literal meaning and should be conveyed adequately in translation. We propose to use lexical formality models to control the formality level of machine translation output. We demonstrate the effectiveness of our approach in empirical evaluations, as measured by automatic metrics and human assessments.


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Can Statistical Post-Editing with a Small Parallel Corpus Save a Weak MT Engine?
Marianna J. Martindale
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Statistical post-editing has been shown in several studies to increase BLEU score for rule-based MT systems. However, previous studies have relied solely on BLEU and have not conducted further study to determine whether those gains indicated an increase in quality or in score alone. In this work we conduct a human evaluation of statistical post-edited output from a weak rule-based MT system, comparing the results with the output of the original rule-based system and a phrase-based statistical MT system trained on the same data. We show that for this weak rule-based system, despite significant BLEU score increases, human evaluators prefer the output of the original system. While this is not a generally conclusive condemnation of statistical post-editing, this result does cast doubt on the efficacy of statistical post-editing for weak MT systems and on the reliability of BLEU score for comparison between weak rule-based and hybrid systems built from them.