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For text dictation it is generally agreed that performance accuracy at a rate below 95% is not acceptable, but this again may be syntax and/or domain specific, e.g. whether there is time pressure on users to complete the task, whether there are alternative methods of completion, and so on.
For example, under such a method applied to plain English text the phrase "I run" counts as one word, but "rhinoceros" and "let's talk" would both count as two. Karat et al. found in one study of average computer users in 1999 that the average rate for transcription was 32.5 words per minute, and 19.0 words per minute for composition. [2]
Transcription should not be confused with translation, which means representing the meaning of text from a source-language in a target language, (e.g. Los Angeles (from source-language Spanish) means The Angels in the target language English); or with transliteration, which means representing the spelling of a text from one script to another.
There are some machine translation evaluation survey works, [17] [18] [19] where people introduced more details about what kinds of human evaluation methods they used and how they work, such as the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness, etc. For automatic evaluations, they also did some clear ...
But there's no way to group two English words producing a single French word. An example of a word-based translation system is the freely available GIZA++ package , which includes the training program for IBM models and HMM model and Model 6. [7] The word-based translation is not widely used today; phrase-based systems are more common.
A rendition of the Vauquois triangle, illustrating the various approaches to the design of machine translation systems.. The direct, transfer-based machine translation and interlingual machine translation methods of machine translation all belong to RBMT but differ in the depth of analysis of the source language and the extent to which they attempt to reach a language-independent ...
Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural machine translation [1] and large language models ...
With the combined power inherent in both systems, coupled with the fact that a Dictionary-Based Machine Translation works best with a "word-for-word bilingual dictionary" [3] lists of words it demonstrates the fact that a coupling of this two translation engines would generate a very powerful translation tool that is, besides being semantically ...
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