Marianna J. Martindale

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We present a simple method for representing text that explicitly encodes differences between two corpora in a domain adaptation or data selection scenario. We do this by replacing every word in the corpora with its part-of-speech tag plus a suffix that indicates the relative bias of the word, or how much likelier it is to be in the task corpus versus the(More)
We describe the University of Maryland machine translation systems submitted to the IWSLT 2015 French-English and Vietnamese-English tasks. We built standard hierarchical phrase-based models, extended in two ways: (1) we applied novel data selection techniques to select relevant information from the large French-English training corpora, and (2) we(More)
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(More)
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(More)
A fundamental part of conducting cross-disciplinary web science research is having useful, high-quality datasets that provide value to studies across disciplines. In this paper, we introduce a large, hand-coded corpus of online harassment data. A team of researchers collaboratively developed a codebook using grounded theory and labeled 35,000 tweets. Our(More)
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