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A finite-state transducer (FST) is a finite-state machine with two memory tapes, following the terminology for Turing machines: an input tape and an output tape. This contrasts with an ordinary finite-state automaton, which has a single tape. An FST is a type of finite-state automaton (FSA) that maps between two sets of symbols. [1]
Here are additional clues for each of the words in today's Mini Crossword. NYT Mini Across Hints. 1 Across: "Give me some _____" — HINT: It starts with the letter "S"
Taking this one stage further, the clue word can hint at the word or words to be abbreviated rather than giving the word itself. For example: "About" for C or CA (for "circa"), or RE. "Say" for EG, used to mean "for example". More obscure clue words of this variety include: "Model" for T, referring to the Model T.
Here are additional clues for each of the words in today's Mini Crossword. NYT Mini Across Hints. 1 Across: "Vertically challenged" — HINT: It starts with the letter "S"
A transducer is a device that converts energy from one form to another. Usually a transducer converts a signal in one form of energy to a signal in another. [1] Transducers are often employed at the boundaries of automation, measurement, and control systems, where electrical signals are converted to and from other physical quantities (energy, force, torque, light, motion, position, etc.).
FST may refer to: Arts and entertainment. Finlands Svenska Television, ... Finite-state transducer; Full-Scale Tunnel, a demolished NASA wind tunnel; Transport
This word ending—thought to be difficult for Spanish speakers to pronounce at the time—evolved in Spanish into a "-te" ending (e.g. axolotl = ajolote). As a rule of thumb, a Spanish word for an animal, plant, food or home appliance widely used in Mexico and ending in "-te" is highly likely to have a Nahuatl origin.
With the advancement of neural networks in natural language processing, it became less common to use FST for morphological analysis, especially for languages for which there is a lot of available training data. For such languages, it is possible to build character-level language models without explicit use of a morphological parser. [1]