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2. DeepL Translator - Wikipedia

   en.wikipedia.org/wiki/DeepL_Translator

   DeepL for Windows translating from Polish to French. The translator can be used for free with a limit of 1,500 characters per translation. Microsoft Word and PowerPoint files in Office Open XML file formats (.docx and .pptx) and PDF files up to 5MB in size can also be translated.

3. Comparison of machine translation applications - Wikipedia

   en.wikipedia.org/wiki/Comparison_of_machine...

   The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.

4. Reverso (language tools) - Wikipedia

   en.wikipedia.org/wiki/Reverso_(language_tools)

   Reverso is a French company specialized in AI-based language tools, translation aids, and language services. [2] These include online translation based on neural machine translation (NMT), contextual dictionaries, online bilingual concordances, grammar and spell checking and conjugation tools.

5. DeepL, long a leader in translation tech, finally embraces LLMs

   www.aol.com/finance/deepl-long-leader...

   For premium support please call: 800-290-4726 more ways to reach us

6. Comparison of computer-assisted translation tools - Wikipedia

   en.wikipedia.org/wiki/Comparison_of_computer...

   A number of computer-assisted translation software and websites exists for various platforms and access types. According to a 2006 survey undertaken by Imperial College of 874 translation professionals from 54 countries, primary tool usage was reported as follows: Trados (35%), Wordfast (17%), Déjà Vu (16%), SDL Trados 2006 (15%), SDLX (4%), STAR Transit [fr; sv] (3%), OmegaT (3%), others (7%).

7. Neural machine translation - Wikipedia

   en.wikipedia.org/wiki/Neural_machine_translation

   Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text. This dataset can contain documents in many languages, but is in practice dominated by English text. [36]