| 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. |
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MMPE: A Multi-Modal Interface for Post-Editing Machine Translation (2020.acl-main)
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Nico Herbig, Tim Düwel, Santanu Pal, Kalliopi Meladaki, Mahsa Monshizadeh, Antonio Krüger, Josef van Genabith
| 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. |
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation (2020.acl-demos)
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Nico Herbig, Santanu Pal, Tim Düwel, Kalliopi Meladaki, Mahsa Monshizadeh, Vladislav Hnatovskiy, Antonio Krüger, Josef van Genabith
| 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. |
Touch Editing: A Flexible One-Time Interaction Approach for Translation (2020.aacl-main)
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| Challenge: | Existing methods for machine translation require intensive keyboard interaction, which is inconvenient on mobile devices. |
| Approach: | They propose a touch-based editing method that is more flexible than keyboard-mouse-based translation postediting. |
| Outcome: | The proposed method significantly outperforms existing interactive translation methods on translation datasets and on post-editing datasets. |
OpenTIPE: An Open-source Translation Framework for Interactive Post-Editing Research (2023.acl-demo)
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| Challenge: | Recent advances in machine translation have not yet improved translation quality . human post-editors must review and post- edit the output to ensure high-quality translations . current approaches do not consider the human interactions that occur in real post- editing scenarios. |
| Approach: | They propose a flexible and extensible framework that supports research on interactive post-editing. |
| Outcome: | The proposed framework aims to support research on interactive post-editing . it showcases its main functionalities with a demonstration video and an online live demo . |
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach (D18-1)
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| Challenge: | Existing approaches to inducing APE have suffered from over-correction, where the APE system tends to keep the machine translated text without any modification. |
| Approach: | They propose a neural programmer-interpreter approach to automated post-editing (APE) that mimics human perform post- editing using discrete edit operations . their model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
| Outcome: | The proposed model outperforms previous neural models for inducing PE programs on the WMT17 APE task for German-English up to +1 BLEU score and -0.7 TER scores. |
Translation in the Hands of Many: Centering Lay Users in Machine Translation Interactions (2025.emnlp-main)
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| Challenge: | Multilingual demands and accessibility have made MT a global tool . however, the understanding of MT consumed by such a diverse group of users remains limited. |
| Approach: | They first trace the evolution of MT user profiles, focusing on non-experts and how their engagement with technology may shift with the rise of LLMs. |
| Outcome: | The proposed approach will help to align MT with user needs and improve the quality of the language. |
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)
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| Challenge: | Using neural machine translation to approximate human parity is difficult due to the lack of parallel training corpora. |
| Approach: | They propose an end-to-end deep learning framework for quality estimation and automatic post-editing of machine translation output. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the English–German dataset and human translators can significantly expedite their post-editing processing with the model. |
Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages (2020.coling-main)
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| Challenge: | Gesture typing is a method of typing words on a touch-based keyboard by drawing a continuous trace passing through the relevant keys. |
| Approach: | They propose a keyboard that supports gesture typing in Indic languages by drawing a continuous trace over the keyboard and the finger needs to be lifted only once a word is completed. |
| Outcome: | The proposed model performs path decoding, transliteration and transliterations correction. |
Leveraging GPT-4 for Automatic Translation Post-Editing (2023.findings-emnlp)
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| Challenge: | Neural Machine Translation models still require translation post-editing to rectify errors and enhance quality under critical settings. |
| Approach: | They use GPT-4 to automatically post-edit NMT outputs across several language pairs . they show that GPT4 is adept at translation post- editing, producing meaningful edits . |
| Outcome: | The proposed translation post-editor improves on state-of-the-art language models on English-Chinese, English-German, Chinese-English and German-English language pairs. |
Automatic Input Rewriting Improves Translation with Large Language Models (2025.naacl-long)
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| Challenge: | LLMs can rewrite inputs but in machine translation, they are primarily used to re-write outputs via post-editing. |
| Approach: | They propose to use LLMs to rewrite inputs automatically to improve machine translation (MT) they propose to simplify inputs and use quality estimation to assess translatability. |
| Outcome: | The proposed methods can be improved by using quality estimation to assess translatability. |