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We propose a new paradigm for machine translation that is particularly useful for no-resource languages (those without any publicly available bilingual or monolingual corpora): LLM-RBMT (LLM-Assisted Rule Based Machine Translation).
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Workflows for kickstarting RBMT in virtually no-resource situation
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Leveraging rule-based machine translation knowledge for under-resourced neural machine translation models
Daniel Torregrosa, Nivranshu Pasricha, Maraim Masoud, Bharathi Raja Chakravarthi, Juan A. Alonso, Noe Casas, and Mihael Arcan. 2019 · 2019
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Making monolingual sentence embeddings multilingual using knowledge distillation
Nils Reimers and Iryna Gurevych. 2020 · 2020
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Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages
Tanmai Khanna, Jonathan N. Washington, Francis M. Tyers, Sevilay Bayatlı, Daniel G. Swanson, Tommi A. Pirinen, Irene Tang, and Hèctor Alòs i Font. 2021 · 2021
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Sentsim: Crosslingual semantic evaluation of machine translation
Yurun Song, Junchen Zhao, and Lucia Specia. 2021 · 2021
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Semmt: A semantic-based testing approach for machine translation systems
Jialun Cao, Meiziniu Li, Yeting Li, Ming Wen, Shing-Chi Cheung, and Haiming Chen. 2022 · 2022
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery et al. 2022 · 2022
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Access and empowerment in digital language learning, maintenance, and revival: a critical literature review
Joshua Taylor and Timothy Kochem. 2022 · 2022
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GPT-4 Technical Report
OpenAI. 2023 · 2023
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Neural machine translation for low-resource languages: A survey
Surangika Ranathunga, En-Shiun Annie Lee, Marjana Prifti Skenduli, Ravi Shekhar, Mehreen Alam, and Rishemjit Kaur. 2023 · 2023
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ChatGPT MT: Competitive for High- (but not Low-) Resource Languages
Nathaniel R. Robinson, Perez Ogayo, David R. Mortensen, and Graham Neubig. 2023 · 2023
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Industrial-Strength Natural Language Processing
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Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 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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Séamus Lankford, Haithem Afli, and Andy Way. 2023 · 2023
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Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. 2023 · 2029
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