Automatic Post-Editing for Machine Translation
Rajen Chatterjee

TL;DR
This paper thoroughly investigates Automatic Post-Editing (APE) for machine translation, exploring its potential, technological advancements, and real-time learning to improve translation quality despite data limitations.
Contribution
It advances APE technology from classical to deep learning methods, addresses data scarcity, and introduces online learning for real-time adaptation.
Findings
Achieved best system at 2017 APE shared task.
Developed online learning framework with distinguished paper award.
Enhanced neural decoding leveraging external knowledge.
Abstract
Automatic Post-Editing (APE) aims to correct systematic errors in a machine translated text. This is primarily useful when the machine translation (MT) system is not accessible for improvement, leaving APE as a viable option to improve translation quality as a downstream task - which is the focus of this thesis. This field has received less attention compared to MT due to several reasons, which include: the limited availability of data to perform a sound research, contrasting views reported by different researchers about the effectiveness of APE, and limited attention from the industry to use APE in current production pipelines. In this thesis, we perform a thorough investigation of APE as a downstream task in order to: i) understand its potential to improve translation quality; ii) advance the core technology - starting from classical methods to recent deep-learning based solutions;…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Semantic Web and Ontologies
