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  2. Transfer-appropriate processing - Wikipedia

    en.wikipedia.org/.../Transfer-appropriate_processing

    Transfer-appropriate processing (TAP) is a type of state-dependent memory specifically showing that memory performance is not only determined by the depth of processing (where associating meaning with information strengthens the memory; see levels-of-processing effect), but by the relationship between how information is initially encoded and how it is later retrieved.

  3. Levels of Processing model - Wikipedia

    en.wikipedia.org/wiki/Levels_of_Processing_model

    Depth of processing falls on a shallow to deep continuum. [citation needed] Shallow processing (e.g., processing based on phonemic and orthographic components) leads to a fragile memory trace that is susceptible to rapid decay. Conversely, deep processing (e.g., semantic processing) results in a more durable memory trace. [1]

  4. Encoding (memory) - Wikipedia

    en.wikipedia.org/wiki/Encoding_(memory)

    They claimed that the level of processing information was dependent upon the depth at which the information was being processed; mainly, shallow processing and deep processing. According to Craik and Lockhart, the encoding of sensory information would be considered shallow processing, as it is highly automatic and requires very little focus.

  5. Machine translation - Wikipedia

    en.wikipedia.org/wiki/Machine_translation

    They can be approximately divided into "shallow" approaches and "deep" approaches. Shallow approaches assume no knowledge of the text. They simply apply statistical methods to the words surrounding the ambiguous word. Deep approaches presume a comprehensive knowledge of the word. So far, shallow approaches have been more successful. [35]

  6. Semantic parsing - Wikipedia

    en.wikipedia.org/wiki/Semantic_parsing

    Deep semantic parsing, also known as compositional semantic parsing, is concerned with producing precise meaning representations of utterances that can contain significant compositionality. [23] Shallow semantic parsers can parse utterances like "show me flights from Boston to Dallas" by classifying the intent as "list flights", and filling ...

  7. Google Neural Machine Translation - Wikipedia

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

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 By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder.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

  8. Transfer-based machine translation - Wikipedia

    en.wikipedia.org/wiki/Transfer-based_machine...

    Superficial transfer (or syntactic). This level is characterised by transferring "syntactic structures" between the source and target languages. It is suitable for languages in the same family or of the same type, for example in the Romance languages between Spanish, Catalan, French, Italian, etc. Deep transfer (or semantic). This level ...

  9. Object copying - Wikipedia

    en.wikipedia.org/wiki/Object_copying

    In VBA, an assignment of variables of type Object is a shallow copy, an assignment for all other types (numeric types, String, user defined types, arrays) is a deep copy. So the keyword Set for an assignment signals a shallow copy and the (optional) keyword Let signals a deep copy. There is no built-in method for deep copies of Objects in VBA.