Notes:
Example-based machine translation (EBMT) is a type of machine translation that relies on a bilingual corpus (a collection of texts in two languages that have been translated from one language to the other) to translate texts from one language to another. The method is called “example-based” because it relies on examples of previously translated sentences to guide the translation process. EBMT systems typically use parallel texts (texts in both languages that correspond to each other word for word) as their main knowledge base at runtime, meaning that they access and use this information while they are translating a text. EBMT is generally considered to be less accurate than other machine translation methods, such as statistical machine translation (SMT) or neural machine translation (NMT), but it can be faster and easier to develop, making it a useful tool in certain situations.
Example-based machine translation (EBMT) can be used to enable dialog systems (computer systems that are designed to communicate with humans through natural language conversations) to communicate with users in multiple languages. In this case, the EBMT system would be used to translate the user’s input from their native language into the language that the dialog system is programmed to understand, and to translate the dialog system’s responses back into the user’s native language.
To use EBMT in a dialog system, the system would need to be configured with a bilingual corpus that includes examples of previously translated sentences or phrases. The system would then use this corpus to identify the most appropriate translation for the user’s input and generate a response in the appropriate language.
One potential advantage of using EBMT in a dialog system is that it can allow the system to communicate with users in multiple languages without the need to build separate versions of the system for each language. However, it is important to note that EBMT systems are generally less accurate than other machine translation methods, such as statistical machine translation (SMT) or neural machine translation (NMT), and may not be suitable for all applications.
Wikipedia:
See also:
100 Best GitHub: Machine Translation | RBMT (Rule-Based Machine Translation) & Dialog Systems | SMT (Statistical Machine Translation) & Dialog Systems 2015
Example-based dialog modeling for practical multi-domain dialog system C Lee, S Jung, S Kim, GG Lee – Speech Communication, 2009 – Elsevier … 1). However, because of the poor adaptability of knowledge-based approaches to multi-domain dialog systems, the number of situation … EBDM was inspired by example-based machine translation (EBMT) (Nagao, 1984), a translation system in which the source sentence can be … Cited by 78 Related articles All 9 versions
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Statistical dialog management methodologies for real applications D Griol, Z Callejas, R López-Cózar – … of the 11th Annual Meeting of the …, 2010 – dl.acm.org … The fi- nal objective is to develop dialog systems that have a more robust behavior and are easier to adapt to … are weighted using a mixture of expert knowledge and data-driven measures by using an agenda and an example-based machine translation approach respectively. … Cited by 9 Related articles All 12 versions
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