Papers by Qu Cui
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation (2022.acl-long)
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| Challenge: | Existing IMT systems relying on lexical constrained decoding (LCD) are limited in translation efficiency and quality due to LCD. |
| Approach: | They propose a novel interactive neural machine translation system that uses lexical constraints to decode missing words in a manually revised translation. |
| Outcome: | The proposed system performs significantly better and faster than state-of-the-art IMT on three translation tasks. |
Fast and Accurate Neural Machine Translation with Translation Memory (2021.acl-long)
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| Challenge: | Existing knowledge demonstrates the superiority of TM-based neural machine translation only on TM specialized tasks . |
| Approach: | They propose a translation memory-based approach to machine translation using a single bilingual sentence as its TM. |
| Outcome: | The proposed approach surpasses baselines on two general tasks and improves on the TM-specialized translation tasks. |
Cross-lingual Contextualized Phrase Retrieval (2024.findings-emnlp)
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| Challenge: | Phrase-level dense retrieval has shown many appealing characteristics in downstream NLP tasks. |
| Approach: | They propose a task formulation of dense retrieval, cross-lingual contextualized phrase retrieval . they extract pairs of cross-linguistic phrases using word alignment information . |
| Outcome: | The proposed task formulation surpasses baselines on the phrase retrieval task and a downstream task, i.e., machine translation, and achieves top-1 accuracy 13 points higher. |