Papers by Nico Herbig

5 papers
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation (2020.acl-demos)

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Challenge: a shift from traditional translation to post-editing (PE) of machine-translated text can save time and reduce errors, but it also affects the design of translation interfaces.
Approach: They propose a prototype that combines traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated (MT) they propose to use these modalités to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place.
Outcome: The proposed interfaces can be used to cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place.
MMPE: A Multi-Modal Interface for Post-Editing Machine Translation (2020.acl-main)

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Challenge: Current advances in machine translation (MT) increase the need for translators to switch from traditional translation to post-editing (PE) of machine-translated text.
Approach: They propose to combine traditional input modes with pen, touch, and speech modalities for post-editing of machine-translated text.
Outcome: The proposed interfaces are designed to reduce errors and save time.
Mid-Air Hand Gestures for Post-Editing of Machine Translation (2021.acl-long)

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Challenge: In a well-connected world, translation is of everincreasing importance.
Approach: They propose to use mid-air hand gestures in combination with the keyboard for editing in machine translation and post-editing workflows to improve quality.
Outcome: The proposed prototype supports mid-air hand gestures for cursor placement, text selection, deletion, and reordering.
Investigating the Helpfulness of Word-Level Quality Estimation for Post-Editing Machine Translation Output (2021.emnlp-main)

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Challenge: Post-editing (PE) machine translation (MT) output can save time and reduce errors.
Approach: They propose to use automatic word-level quality estimation to predict correctness of MT output to flag problematic output.
Outcome: The proposed model is not good enough to support human translations, but is based on a visualization reflecting uncertainty of the model.
The Transference Architecture for Automatic Post-Editing (2020.coling-main)

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Challenge: A research challenge is the search for architectures that best support the capture, preparation and provision of src and mt information and its integration with pe decisions.
Approach: They propose a multi-encoder based neural APE model that conditions post-editing decisions on both the source and machine translated text as inputs.
Outcome: The proposed model outperforms the best performing systems by 1 BLEU point on the WMT 2016, 2017, and 2018 English–German APE shared tasks.

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