Automatic Post-Editing
25 papers with code • 0 benchmarks • 10 datasets
Automatic post-editing (APE) is used to correct errors in the translation made by the machine translation systems.
Benchmarks
These leaderboards are used to track progress in Automatic Post-Editing
Datasets
eSCAPE
WMT 2016 News
WMT 2018 News
SubEdits
DivEMT
WMT 2016 IT
WMT 2016 Biomedical
WMT 2015 News
APE
Most implemented papers
Automatic Post-Editing for Vietnamese
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation.
Transfer Learning for Sequence Generation: from Single-source to Multi-source
Although directly finetuning pretrained models on MSG tasks and concatenating multiple sources into a single long sequence is regarded as a simple method to transfer pretrained models to MSG tasks, we conjecture that the direct finetuning method leads to catastrophic forgetting and solely relying on pretrained self-attention layers to capture cross-source information is not sufficient.
Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study
osu-nlp-group/llm-cn-eval
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Findings (ACL) 2022
In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in English.
Automatic Correction of Human Translations
We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors.
