🤖 AI Summary
This work addresses the challenge of accurately capturing deep semantic transformations in language pairs with substantial structural divergence, such as Arabic–English, where existing automated tools often fall short. The authors propose an interactive translation annotation method grounded in large language models (LLMs), which employs a templated prompt manager to generate controlled, JSON-formatted sentence segmentation and alignment outputs. By integrating a human-in-the-loop verification process, the approach innovatively combines the scalability of LLMs with the precision of expert annotation. A marginalia-style architecture enables fine-grained labeling of translation strategies, significantly enhancing the quality of parallel corpora for complex language pairs while maintaining annotation efficiency. This framework effectively bridges the gap between fully automated systems and high-quality manual annotation.
📝 Abstract
The construction of high-quality parallel corpora for translation research has increasingly evolved from simple sentence alignment to complex, multi-layered annotation tasks. This methodological shift presents significant challenges for structurally divergent language pairs, such as Arabic--English, where standard automated tools frequently fail to capture deep linguistic shifts or semantic nuances. This paper introduces a novel, LLM-assisted interactive tool designed to reduce the gap between scalable automation and the rigorous precision required for expert human judgment. Unlike traditional statistical aligners, our system employs a template-based Prompt Manager that leverages large language models (LLMs) for sentence segmentation and alignment under strict JSON output constraints. In this tool, automated preprocessing integrates into a human-in-the-loop workflow, allowing researchers to refine alignments and apply custom translation technique annotations through a stand-off architecture. By leveraging LLM-assisted processing, the tool balances annotation efficiency with the linguistic precision required to analyze complex translation phenomena in specialized domains.