PET: a Tool for Post-editing and Assessing Machine Translation

By Wilker Aziz, Sheila Castilho, Lucia Specia. 2012

163 citations recorded by the source corpus.

Given the significant improvements in Machine Translation (MT) quality and the increasing demand for translations, post-editing of automatic translations is becoming a popular practice in the translation industry. It has been shown to allow for much larger volumes of translations to be produced, saving time and costs. In addition, the post-editing of automatic translations can help understand problems in such translations and this can be used as feedback for researchers and developers to improve MT systems. Finally, post-editing can be used as a way of evaluating the quality of translations in terms of how much post-editing effort these translations require. We describe a standalone tool that has two main purposes: facilitate the post-editing of translations from any MT system so that they reach publishable quality and collect sentence-level information from the post-editing process, e.g.: post-editing time and detailed keystroke statistics.

Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, methodology): “are a manual revision of the target, done by professional translators using the PET post-editing tool (Aziz et al., 2012).”
Accurate Evaluation of Segment-level Machine Translation Metrics (2015, background): “In the rare cases where significance tests have been applied, tests or confidence intervals for individual correlations form the basis for drawing conclusions (Aziz et al., 2012; Machacek and Bojar, 2014).”
Evaluation of Text Generation: A Survey (2020, background): “Post-editing, most often seen in machine translation (Aziz et al., 2012; Denkowski et al., 2014), can also be used to measure a system’s success by measuring how many changes a person makes to a machine-generated text.”
A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models (2022, background): “…perspective (such as whether or not it leads to an improved decision-making or text comprehension accuracy [39], typically through a User Task Success evaluation), or from a system’s perspective [3, 26], such as the performance of a dialogue system through the System Purpose Success evaluation.”
compare-mt: A Tool for Holistic Comparison of Language Generation Systems (2019, background): “…outputs of multiple systems, through visualization or highlighting of salient parts of the output (Lopez and Resnik, 2005; Stymne, 2011; Zeman et al., 2011; Madnani, 2011; Aziz et al., 2012; Gonz`alez et al., 2012; Federmann, 2012; Chatzitheodorou and Chatzis-tamatis, 2013; Klejch et al., 2015).”