Shou-Yi Hung
2025
TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance
Syed Mekael Wasti
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Shou-Yi Hung
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Christopher Collins
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En-Shiun Annie Lee
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Machine translation (MT) post-editing and research data collection often rely on inefficient, disconnected workflows. We introduce **TranslationCorrect**, an integrated framework designed to streamline these tasks. **TranslationCorrect** combines MT generation using models like NLLB, automated error prediction using models like XCOMET or LLM APIs (providing detailed reasoning), and an intuitive post-editing interface within a single environment. Built with human-computer interaction (HCI) principles in mind to minimize cognitive load, as confirmed by a user study. For translators, it enables them to correct errors and batch translate efficiently. For researchers, **TranslationCorrect** exports high-quality span-based annotations in the Error Span Annotation (ESA) format, using an error taxonomy inspired by Multidimensional Quality Metrics (MQM). These outputs are compatible with state-of-the-art error detection models and suitable for training MT or post-editing systems. Our user study confirms that **TranslationCorrect** significantly improves translation efficiency and user satisfaction over traditional annotation methods.
ATAIGI: An AI-Powered Multimodal Learning App Leveraging Generative Models for Low-Resource Taiwanese Hokkien
Yun-Hsin Chu
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Shuai Zhu
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Shou-Yi Hung
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Bo-Ting Lin
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En-Shiun Annie Lee
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Richard Tzong-Han Tsai
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
Many endangered languages are at risk of extinction due to barriers in communication and generational gaps that hinder their preservation. A cause for languages becoming endangered is the lack of language educational tools and artificial intelligence (AI) models for these low-resource languages. To address this, we propose the ATAIGI learning app designed with AI-powered models leveraging multimodal generative techniques. Our app offers users a comprehensive learning experience by providing translated phrases and definitions, example sentences, illustrative images, romanized pronunciation, and audio speech to accelerate language learning. ATAIGI is built on five AI models that are rigorously benchmarked individually, with our Transliteration Model achieving state-of-the-art results for Taiwanese Hokkien transliteration. ATAIGI is available for all to learn the endangered language of Taiwanese Hokkien, an endangered language spoken in Taiwan. A human evaluation conducted demonstrates the effectiveness of ATAIGI in improving language proficiency and cultural understanding, supporting its potential for the preservation and education of endangered languages like the Taiwanese Hokkien.
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- En-Shiun Annie Lee 2
- Yun-Hsin Chu 1
- Christopher Collins 1
- Bo-Ting Lin 1
- Richard Tzong-Han Tsai 1
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