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 .

Similar Papers

Interactive Post-Editing for Verbosity Controlled Translation (2022.coling-1)

Copied to clipboard

Challenge: Recent machine translation models have shown to excel with aspects of translation quality like adequacy and fluency but these models still suffer notable shortcomings like out-of-domain data, low-resource languages, rare words and longer sentences.
Approach: They propose to use human-in-loop interactive post-editing models to improve translation quality and rephrase the text with a desired style variation.
Outcome: The proposed model achieves BERTScore over state-of-the-art machine translation models while maintaining the desired token-level and verbosity preference.
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)

Copied to clipboard

Challenge: Existing systems that use a left-to-right completion paradigm are inefficient and expensive.
Approach: They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation .
Outcome: The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems .
Touch Editing: A Flexible One-Time Interaction Approach for Translation (2020.aacl-main)

Copied to clipboard

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.
QuickEdit: Editing Text & Translations by Crossing Words Out (N18-1)

Copied to clipboard

Challenge: Using statistical learning, a computer can rephrase a sentence by only pointing at words that should be avoided.
Approach: They propose a framework for computer-assisted text editing that relies on simple interactions between human editors and tokens.
Outcome: The proposed framework allows to get substantial modifications to a sentence without human intervention.
TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance (2025.acl-demo)

Copied to clipboard

Challenge: Current workflows for machine translation (MT) post-editing and research data collection are inefficient and time-consuming.
Approach: They propose a framework that combines MT and error prediction within a single environment.
Outcome: **TranslationCorrect** exports high-quality span-based annotations in the Error Span Annotation format, using an error taxonomy inspired by Multidimensional Quality Metrics (MQM).
Computer Assisted Translation with Neural Quality Estimation and Automatic Post-Editing (2020.findings-emnlp)

Copied to clipboard

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.
IntelliCAT: Intelligent Machine Translation Post-Editing with Quality Estimation and Translation Suggestion (2021.acl-demo)

Copied to clipboard

Challenge: Existing computer-aided translation tools require the translator to edit incorrect parts of a document, while ITP tools require fewer edits.
Approach: They propose an interactive translation interface with neural models that streamline the post-editing process on machine translation output.
Outcome: The proposed interface can significantly improve translation quality and a user study shows that it speeds up the post-editing process by 52.9% compared to translating from scratch.
Building The First English-Brazilian Portuguese Corpus for Automatic Post-Editing (2020.coling-main)

Copied to clipboard

Challenge: Existing corpus for automatic post-editing of English and Brazilian Portuguese is limited.
Approach: They introduce a corpus for Automatic Post-Editing of English and Brazilian Portuguese.
Outcome: The proposed corpus improves on the English and Brazilian Portuguese languages.
Mid-Air Hand Gestures for Post-Editing of Machine Translation (2021.acl-long)

Copied to clipboard

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.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits (2023.findings-eacl)

Copied to clipboard

Challenge: Existing neural machine translation models ignore personal style in their translations, but in these studies the definition of personal style is over-simplified.
Approach: They propose a personalized automatic post-editing framework that generates sentences considering distinct personal behaviors by collecting post-edited data from a live machine translation system and combining a discriminator module and user-specific parameters.
Outcome: The proposed model outperforms baseline models on four different metrics including BLEU, TER, YiSi-1, and human evaluation.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations