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Large language models (LLMs) demonstrate remarkable machine translation (MT) abilities via prompting, even though they were not explicitly trained for this task.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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Code-switching for enhancing nmt with pre-specified translation
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Training neural machine translation to apply terminology constraints
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Masoud Jalili Sabet, Philipp Dufter, François Yvon, and Hinrich Schütze. 2020 · 2004
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Nearest neighbor machine translation
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Look it up: Bilingual and monolingual dictionaries improve neural machine translation
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Incorporating discrete translation lexicons into neural machine translation
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Bridging neural machine translation and bilingual dictionaries
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Word translation without parallel data
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Lexically constrained decoding for sequence generation using grid beam search
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Six challenges for neural machine translation
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A call for clarity in reporting BLEU scores
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Fast lexically constrained decoding with dynamic beam allocation for neural machine translation
Matt Post and David Vilar. 2018 · 2018
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A template based approach for training nmt for low-resource uralic languages-a pilot with finnish
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Unsupervised domain clusters in pretrained language models
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Bilingual lexicon induction via unsupervised bitext construction and word alignment
Haoyue Shi, Luke Zettlemoyer, and Sida I Wang. 2021 · 2021
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Non-parametric unsupervised domain adaptation for neural machine translation
Xin Zheng, Zhirui Zhang, Shujian Huang, Boxing Chen, Jun Xie, Weihua Luo, and Jiajun Chen. 2021 · 2021
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In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2022 · 2022
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Using natural language prompts for machine translation
Xavier Garcia and Orhan Firat. 2022 · 2022
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The flores-101 evaluation benchmark for low-resource and multilingual machine translation
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