Unsupervised Machine Translation: How Machines Learn to Understand Across Languages
For decades, machine translation between natural languages fundamentally relied on human-translated documents known as parallel texts, which provide direct correspondences between source and target sentences. The notion that translation systems could be trained on non-parallel texts, independently w...
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
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Karolinum Press
2025
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| Truy cập trực tuyến: | https://directory.doabooks.org/handle/20.500.12854/161895 |
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| _version_ | 1869519335716814848 |
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| author | Kvapilíková, Ivana |
| author_browse | Kvapilíková, Ivana |
| author_facet | Kvapilíková, Ivana |
| author_sort | Kvapilíková, Ivana |
| collection | Directory of Open Access Books |
| description | For decades, machine translation between natural languages fundamentally relied on human-translated documents known as parallel texts, which provide direct correspondences between source and target sentences. The notion that translation systems could be trained on non-parallel texts, independently written in different languages, was long considered unrealistic. Fast forward to the era of large language models (LLMs), and we now know that given their sufficient computational resources, LLMs exploit incidental parallelism in their vast training data, i.e., they identify parallel messages across languages and learn to translate without explicit supervision. LLMs have since demonstrated the ability to perform translation tasks with impressive quality, rivaling systems specifically trained for translation.
This monograph explores the fascinating journey that led to this point, focusing on the development of unsupervised machine translation. Long before the rise of LLMs, researchers were exploring the idea that translation could be achieved without parallel data. Their efforts centered on motivating models to discover cross-lingual correspondences through various techniques, such as the mapping of word embedding spaces, back-translation, or parallel sentence mining. Although much of the research described in this monograph predates the mainstream adoption of LLMs, the insights gained remain highly relevant. They offer a foundation for understanding how and why LLMs are able to translate. |
| format | Online |
| id | doab-20.500.12854ir-161895 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Karolinum Press |
| publisherStr | Karolinum Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1618952025-07-02T09:28:44Z Unsupervised Machine Translation: How Machines Learn to Understand Across Languages Kvapilíková, Ivana linguistics translation language LLM machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics For decades, machine translation between natural languages fundamentally relied on human-translated documents known as parallel texts, which provide direct correspondences between source and target sentences. The notion that translation systems could be trained on non-parallel texts, independently written in different languages, was long considered unrealistic. Fast forward to the era of large language models (LLMs), and we now know that given their sufficient computational resources, LLMs exploit incidental parallelism in their vast training data, i.e., they identify parallel messages across languages and learn to translate without explicit supervision. LLMs have since demonstrated the ability to perform translation tasks with impressive quality, rivaling systems specifically trained for translation. This monograph explores the fascinating journey that led to this point, focusing on the development of unsupervised machine translation. Long before the rise of LLMs, researchers were exploring the idea that translation could be achieved without parallel data. Their efforts centered on motivating models to discover cross-lingual correspondences through various techniques, such as the mapping of word embedding spaces, back-translation, or parallel sentence mining. Although much of the research described in this monograph predates the mainstream adoption of LLMs, the insights gained remain highly relevant. They offer a foundation for understanding how and why LLMs are able to translate. Published 2025-07-02T09:28:42Z 2025-07-02T09:28:42Z 2025-06 chapter 9788024660783 https://directory.doabooks.org/handle/20.500.12854/161895 eng image/jpeg Attribution 4.0 International https://karolinum.cz/knihy/kvapilikova-unsupervised-machine-translation-how-machines-learn-to-understand-across-languages-31561 https://dspace.cuni.cz/bitstream/handle/20.500.11956/198746/9788024660844.pdf Karolinum Press 10.14712/9788024660844 10.14712/9788024660844 8d94c6aa-2a71-4559-9698-bf3e883a574a 9788024660783 176 Prague open access |
| spellingShingle | linguistics translation language LLM machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics Kvapilíková, Ivana Unsupervised Machine Translation: How Machines Learn to Understand Across Languages |
| title | Unsupervised Machine Translation: How Machines Learn to Understand Across Languages |
| title_full | Unsupervised Machine Translation: How Machines Learn to Understand Across Languages |
| title_fullStr | Unsupervised Machine Translation: How Machines Learn to Understand Across Languages |
| title_full_unstemmed | Unsupervised Machine Translation: How Machines Learn to Understand Across Languages |
| title_short | Unsupervised Machine Translation: How Machines Learn to Understand Across Languages |
| title_sort | unsupervised machine translation how machines learn to understand across languages |
| topic | linguistics translation language LLM machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics |
| topic_facet | linguistics translation language LLM machine translation thema EDItEUR::C Language and Linguistics::CF Linguistics::CFX Computational and corpus linguistics |
| url | https://directory.doabooks.org/handle/20.500.12854/161895 |
| work_keys_str_mv | AT kvapilikovaivana unsupervisedmachinetranslationhowmachineslearntounderstandacrosslanguages |