Ke Wu


2023

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Long-Form Speech Translation through Segmentation with Finite-State Decoding Constraints on Large Language Models
Arya McCarthy | Hao Zhang | Shankar Kumar | Felix Stahlberg | Ke Wu
Findings of the Association for Computational Linguistics: EMNLP 2023

One challenge in speech translation is that plenty of spoken content is long-form, but short units are necessary for obtaining high-quality translations. To address this mismatch, we adapt large language models (LLMs) to split long ASR transcripts into segments that can be independently translated so as to maximize the overall translation quality. We overcome the tendency of hallucination in LLMs by incorporating finite-state constraints during decoding; these eliminate invalid outputs without requiring additional training. We discover that LLMs are adaptable to transcripts containing ASR errors through prompt-tuning or fine-tuning. Relative to a state-of-the-art automatic punctuation baseline, our best LLM improves the average BLEU by 2.9 points for English–German, English–Spanish, and English–Arabic TED talk translation in 9 test sets, just by improving segmentation.

2013

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Towards Efficient Large-Scale Feature-Rich Statistical Machine Translation
Vladimir Eidelman | Ke Wu | Ferhan Ture | Philip Resnik | Jimmy Lin
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Mr. MIRA: Open-Source Large-Margin Structured Learning on MapReduce
Vladimir Eidelman | Ke Wu | Ferhan Ture | Philip Resnik | Jimmy Lin
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2008

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Cross Language Text Categorization Using a Bilingual Lexicon
Ke Wu | Xiaolin Wang | Bao-Liang Lu
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I