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By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. [10] GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2]
Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3]
Google Translator Toolkit by default used Google Translate to automatically pre-translate uploaded documents which translators could then improve. Google Inc released Google Translator Toolkit on June 8, 2009. [2] This product was expected to be named Google Translation Center, as had been announced in August 2008.
The Translate Toolkit is a localization and translation toolkit. It provides a set of tools for working with localization file formats and files that might need localization. The toolkit also provides an API on which to develop other localization tools.
# imports from jax import jit import jax.numpy as jnp # define the cube function def cube (x): return x * x * x # generate data x = jnp. ones ((10000, 10000)) # create the jit version of the cube function jit_cube = jit (cube) # apply the cube and jit_cube functions to the same data for speed comparison cube (x) jit_cube (x)
1.1.0: Yes: 400+ Competitive performance for Chinese translation tasks; statistical machine translation. Supports phrase-based, hierarchical phrase-based, and syntax-based (string-to-tree, tree-to-string, and tree-to-tree) models for research purposes. OpenLogos: Windows, Linux: GPL or paid initiative taker: No fee required: 1.0.3: Yes: Rule ...
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning .
Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning , respectively. For example, the following prompts were used by Hendy et al. (2023) for zero-shot and one-shot translation: [ 35 ]