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  2. Machine translation of sign languages - Wikipedia

    en.wikipedia.org/wiki/Machine_translation_of...

    Sign language translation technologies are limited in the same way as spoken language translation. None can translate with 100% accuracy. In fact, sign language translation technologies are far behind their spoken language counterparts. This is, in no trivial way, due to the fact that signed languages have multiple articulators.

  3. Semantic parsing - Wikipedia

    en.wikipedia.org/wiki/Semantic_parsing

    Technologies related to accessibility: Helps create tools for the disabled, such as sign language interpretation and text to speech conversion. Legal and Healthcare Informatics: Semantic parsing can extract and structure important information from legal documents and medical records to support research and decision-making.

  4. Sign language recognition - Wikipedia

    en.wikipedia.org/wiki/Sign_language_recognition

    Sign Language Recognition (shortened generally as SLR) is a computational task that involves recognizing actions from sign languages. [1] This is an essential problem to solve especially in the digital world to bridge the communication gap that is faced by people with hearing impairments.

  5. Google Neural Machine Translation - Wikipedia

    en.wikipedia.org/wiki/Google_Neural_Machine...

    By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder.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 ]

  6. Natural language understanding - Wikipedia

    en.wikipedia.org/wiki/Natural_language_understanding

    Natural language understanding (NLU) or natural language interpretation (NLI) [1] is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem.

  7. Neural machine translation - Wikipedia

    en.wikipedia.org/wiki/Neural_machine_translation

    However, since using large language models (LLMs) such as BERT pre-trained on large amounts of monolingual data as a starting point for learning other tasks has proven very successful in wider NLP, this paradigm is also becoming more prevalent in NMT. This is especially useful for low-resource languages, where large parallel datasets do not exist.

  8. Language creation in artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Language_creation_in...

    Researchers examined whether the machine learning algorithms were choosing to translate human-language sentences into a kind of "interlingua", and found that the AI was indeed encoding semantics within its structures. The researchers cited this as evidence that a new interlingua, evolved from the natural languages, exists within the network.

  9. ASL interpreting - Wikipedia

    en.wikipedia.org/wiki/ASL_interpreting

    According to the U.S. Department of Justice, a qualified interpreter is “someone who is able to interpret effectively, accurately, and impartially, both receptively (i.e., understanding what the person with the disability is saying) and expressively (i.e., having the skill needed to convey information back to that person) using any necessary specialized vocabulary.” [2] ASL interpreters ...

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