Zero-Shot Machine Translation: A technique that enables translation between language pairs without direct training data, leveraging shared knowledge from other languages.
Machine translation has made significant progress in recent years, thanks to advancements in deep learning and neural networks. Zero-Shot Machine Translation (ZSMT) is an emerging approach that allows translation between language pairs without direct training data. Instead, it leverages shared knowledge from other languages to perform translations. This technique is particularly useful for under-resourced languages and closely related languages, where training data may be scarce.
Recent research in machine translation has explored various challenges, such as domain mismatch, rare words, long sentences, and word alignment. One study investigated the potential of attention-based neural machine translation for simultaneous translation, introducing a novel decoding algorithm called simultaneous greedy decoding. Another study presented PETCI, a parallel English translation dataset of Chinese idioms, aiming to improve idiom translation for both humans and machines.
Practical applications of machine translation include real-time medical translation, where a Polish-English translation system was developed for medical data using the European Medicines Agency parallel text corpus. Another application is the use of orthographic information to improve machine translation for under-resourced languages. By incorporating orthographic knowledge, researchers have demonstrated improvements in translation performance.
A company case study is Google Translate, which has been tested using a methodology called referentially transparent inputs (RTIs). This approach detects when translations break the property of referential transparency, leading to erroneous translations. By evaluating Google Translate and Bing Microsoft Translator with 200 unlabeled sentences, the study detected a significant number of translation errors.
In conclusion, Zero-Shot Machine Translation holds great potential for improving translation quality, especially for under-resourced languages. By leveraging shared knowledge from other languages and incorporating novel techniques, researchers are making strides in addressing the challenges and complexities of machine translation.
Zero-Shot Machine Translation
Zero-Shot Machine Translation Further Reading
1.Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity http://arxiv.org/abs/2105.04616v1 Yingxue Fu, Mark-Jan Nederhof2.Can neural machine translation do simultaneous translation? http://arxiv.org/abs/1606.02012v1 Kyunghyun Cho, Masha Esipova3.PETCI: A Parallel English Translation Dataset of Chinese Idioms http://arxiv.org/abs/2202.09509v1 Kenan Tang4.Pre-Translation for Neural Machine Translation http://arxiv.org/abs/1610.05243v1 Jan Niehues, Eunah Cho, Thanh-Le Ha, Alex Waibel5.Six Challenges for Neural Machine Translation http://arxiv.org/abs/1706.03872v1 Philipp Koehn, Rebecca Knowles6.Increasing the throughput of machine translation systems using clouds http://arxiv.org/abs/1611.02944v1 Jernej Vičič, Andrej Brodnik7.Testing Machine Translation via Referential Transparency http://arxiv.org/abs/2004.10361v2 Pinjia He, Clara Meister, Zhendong Su8.Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts http://arxiv.org/abs/1509.08644v1 Krzysztof Wołk, Krzysztof Marasek9.A Survey of Orthographic Information in Machine Translation http://arxiv.org/abs/2008.01391v1 Bharathi Raja Chakravarthi, Priya Rani, Mihael Arcan, John P. McCrae10.Keyframe Segmentation and Positional Encoding for Video-guided Machine Translation Challenge 2020 http://arxiv.org/abs/2006.12799v1 Tosho Hirasawa, Zhishen Yang, Mamoru Komachi, Naoaki OkazakiZero-Shot Machine Translation Frequently Asked Questions
What is zero-shot translation?
Zero-shot translation is a technique in machine translation that enables the translation between language pairs without direct training data. It leverages shared knowledge from other languages to perform translations, making it particularly useful for under-resourced languages and closely related languages where training data may be scarce.
What is zero-shot learning neural machine translation?
Zero-shot learning neural machine translation (ZSMT) is an approach that combines zero-shot translation with neural machine translation (NMT) models. It uses deep learning and neural networks to translate between language pairs without direct training data, relying on shared knowledge from other languages to improve translation quality, especially for under-resourced languages.
What is zero-shot translation with NMT?
Zero-shot translation with NMT refers to the application of neural machine translation models to perform zero-shot translation. NMT models are a type of deep learning model that have shown significant improvements in translation quality. By leveraging shared knowledge from other languages, NMT models can perform translations between language pairs without direct training data.
What is the NLP task machine translation?
Machine translation is a natural language processing (NLP) task that involves automatically translating text from one language to another. It is a complex task that requires understanding the syntax, semantics, and context of the source language and generating an accurate and coherent translation in the target language. Machine translation has made significant progress in recent years, thanks to advancements in deep learning and neural networks.
How does zero-shot machine translation work?
Zero-shot machine translation works by leveraging shared knowledge from other languages to perform translations between language pairs without direct training data. This is achieved by training a model on multiple source-target language pairs, allowing it to learn a shared representation of the languages. When translating between a new language pair, the model can use this shared representation to generate translations, even if it has not been explicitly trained on that specific pair.
What are the challenges in zero-shot machine translation?
Some of the challenges in zero-shot machine translation include domain mismatch, rare words, long sentences, and word alignment. These challenges can lead to translation errors and reduced translation quality. Researchers are continuously exploring novel techniques and approaches to address these challenges and improve the performance of zero-shot machine translation systems.
What are some practical applications of zero-shot machine translation?
Practical applications of zero-shot machine translation include real-time medical translation, where systems can be developed to translate medical data between languages without direct training data. Another application is the use of orthographic information to improve machine translation for under-resourced languages, where incorporating orthographic knowledge can lead to improvements in translation performance.
How does Google Translate use zero-shot machine translation?
Google Translate uses zero-shot machine translation to improve translation quality for language pairs with limited training data. By leveraging shared knowledge from other languages, Google Translate can generate translations between language pairs without direct training data, helping to address the challenges of under-resourced languages and closely related languages.
What is the future of zero-shot machine translation?
The future of zero-shot machine translation lies in continued research and development to address the challenges and complexities of the task. By incorporating novel techniques, such as attention-based neural machine translation and simultaneous greedy decoding, researchers aim to improve translation quality and performance, especially for under-resourced languages. Practical applications of zero-shot machine translation will also continue to expand, with potential use cases in various industries and domains.
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