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Translating culture-related content is vital for effective cross-cultural communication.
Language models are few-shot learners
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Training neural machine translation to apply terminology constraints
Georgiana Dinu, Prashant Mathur, Marcello Federico, and Yaser Al-Onaizan. 2019 · 1906
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Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein et al. 1966 · 1966
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Bilingual code-switching and syntactic theory
Ellen Woolford. 1983 · 1983
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A textbook of translation , volume 66
Peter Newmark. 1988 · 1988
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Comparative stylistics of French and English: A methodology for translation , volume 11
Jean-Paul Vinay and Jean Darbelnet. 1995 · 1995
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Comet: A neural framework for mt evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020a · 2009
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MT-based sentence alignment for OCR-generated parallel texts
Rico Sennrich and Martin Volk. 2010 · 2010
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The apposcorpus: A new multilingual, multi-domain dataset for factual appositive generation
Yova Kementchedjhieva, Di Lu, and Joel Tetreault. 2020 · 2011
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Translating culture: problems, strategies and practical realities
Ana Fernández Guerra. 2012 · 2012
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Culture-specific items: Translation procedures for a text about australian and new zealand children’s literature
Ulrika Persson. 2015 · 2015
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Incorporating discrete translation lexicons into neural machine translation
Philip Arthur, Graham Neubig, and Satoshi Nakamura. 2016 · 2016
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Opus-parallel corpora for everyone
Jörg Tiedemann. 2016 · 2016
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Sling: A framework for frame semantic parsing
Michael Ringgaard, Rahul Gupta, and Fernando CN Pereira. 2017 · 2017
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Translating phrases in neural machine translation
Xing Wang, Zhaopeng Tu, Deyi Xiong, and Min Zhang. 2017 · 2017
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With few eyes, all hoaxes are deep
Sumit Asthana and Aaron Halfaker. 2018 · 2018
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Massively multilingual neural machine translation
Roee Aharoni, Melvin Johnson, and Orhan Firat. 2019 · 2019
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Domain adaptation of neural machine translation by lexicon induction
Junjie Hu, Mengzhou Xia, Graham Neubig, and Jaime Carbonell. 2019 · 2019
Cited alongside, same era.
COMET: A neural framework for MT evaluation
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020b · 2020
Cited alongside, same era.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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On the evaluation of machine translation for terminology consistency
Antonios Anastasopoulos, Laurent Besacier, James Cross, Matthias Gallé, Philipp Koehn, Vassilina Nikoulina, et al. 2021 · 2021
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Nearest neighbor machine translation
No language left behind: Scaling human-centered machine translation
NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. 2022 · 2022
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Frmt: A benchmark for few-shot region-aware machine translation
Parker Riley, Timothy Dozat, Jan A Botha, Xavier Garcia, Dan Garrette, Jason Riesa, Orhan Firat, and Noah Constant. 2022 · 2022
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Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
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On the effect of pretraining corpora on in-context learning by a large-scale language model
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Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2021 · 2021
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Cultural and geographical influences on image translatability of words across languages
Nikzad Khani, Isidora Tourni, Mohammad Sadegh Rasooli, Chris Callison-Burch, and Derry Tanti Wijaya. 2021 · 2021
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Adapting entities across languages and cultures
Denis Peskov and Viktor Hangya. 2021 · 2021
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Adapting entities across languages and cultures
Denis Peskov, Viktor Hangya, Jordan Boyd-Graber, and Alexander Fraser. 2021 · 2021
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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
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2022 · 2022
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Challenges and strategies in cross-cultural nlp
Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, et al. 2022 · 2022
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Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, Woomyoung Park, Jung-Woo Ha, and Nako Sung. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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Varepsilon kú mask: Integrating Yorùbá cultural greetings into machine translation
Idris Akinade, Jesujoba Alabi, David Adelani, Clement Odoje, and Dietrich Klakow. 2023 · 2023
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Dictionary-based phrase-level prompting of large language models for machine translation
Marjan Ghazvininejad, Hila Gonen, and Luke Zettlemoyer. 2023 · 2023
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How good are gpt models at machine translation? a comprehensive evaluation
Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matsushita, Young Jin Kim, Mohamed Afify, and Hany Hassan Awadalla. 2023 · 2023
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Is chatgpt a good translator? a preliminary study
Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023 · 2023
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Large language models are state-of-the-art evaluators of translation quality
Tom Kocmi and Christian Federmann. 2023 · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn. 2023 · 2023
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Frmt: A benchmark for few-shot region-aware machine translation
Parker Riley, Timothy Dozat, Jan A Botha, Xavier Garcia, Dan Garrette, Jason Riesa, Orhan Firat, and Noah Constant. 2023 · 2023
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Multilingual machine translation with large language models: Empirical results and analysis
Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. 2023 · 2023
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Cultural adaptation of recipes
Yong Cao, Yova Kementchedjhieva, Ruixiang Cui, Antonia Karamolegkou, Li Zhou, Megan Dare, Lucia Donatelli, and Daniel Hershcovich. 2024 · 2024
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Translate meanings, not just words: Idiomkb’s role in optimizing idiomatic translation with language models
Shuang Li, Jiangjie Chen, Siyu Yuan, Xinyi Wu, Hao Yang, Shimin Tao, and Yanghua Xiao. 2024 · 2024
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