Incremental Speech Translation
Amtrup, Jan W.
Incremental Speech Translation [electronic resource] / by Jan W. Amtrup. - 1st ed. 1999. - XVI, 212 p. online resource. - Lecture Notes in Artificial Intelligence, 1735 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 1735 .
Graph Theory and Natural Language Processing -- Unification-Based Formalisms for Translation in Natural Language Processing -- MILC: Structure and Implementation -- Experiments and Results -- Conclusion and Outlook.
Human language capabilities are based on mental proceduresthat are closely linked to the time domain. Listening, understanding,and reacting, on the one hand, as well as planning,formulating,and speaking,onthe other, are performedin a highlyover lapping manner, thus allowing inter human communication to proceed in a smooth and ?uent way. Although it happens to be the natural mode of human language interaction, in cremental processing is still far from becoming a common feature of today’s lan guage technology. Instead, it will certainly remain one of the big challenges for research activities in the years to come. Usually considered dif?cult to a degree that rendersit almost intractableforpracticalpurposes,incrementallanguageprocessing has recently been attracting a steadily growing interest in the spoken language pro cessing community. Its notorious dif?culty can be attributed mainly to two reasons: Due to the inaccessibility of the right context, global optimization criteria are no longer available. This loss must be compensated for by communicating larger search spaces between system components or by introducing appropriate repair mechanisms. In any case, the complexity of the task can easily grow by an order of magnitude or even more. Incrementality is an almost useless feature as long as it remains a local property of individual system components. The advantages of incremental processing can be effectiveonly if all the componentsof a producer consumerchain consistently adhere to the same pattern of temporal behavior.
9783540467618
10.1007/3-540-46761-0 doi
Natural language processing (Computer science).
Artificial intelligence.
Machine theory.
Natural Language Processing (NLP).
Artificial Intelligence.
Formal Languages and Automata Theory.
QA76.9.N38
006.35
Incremental Speech Translation [electronic resource] / by Jan W. Amtrup. - 1st ed. 1999. - XVI, 212 p. online resource. - Lecture Notes in Artificial Intelligence, 1735 2945-9141 ; . - Lecture Notes in Artificial Intelligence, 1735 .
Graph Theory and Natural Language Processing -- Unification-Based Formalisms for Translation in Natural Language Processing -- MILC: Structure and Implementation -- Experiments and Results -- Conclusion and Outlook.
Human language capabilities are based on mental proceduresthat are closely linked to the time domain. Listening, understanding,and reacting, on the one hand, as well as planning,formulating,and speaking,onthe other, are performedin a highlyover lapping manner, thus allowing inter human communication to proceed in a smooth and ?uent way. Although it happens to be the natural mode of human language interaction, in cremental processing is still far from becoming a common feature of today’s lan guage technology. Instead, it will certainly remain one of the big challenges for research activities in the years to come. Usually considered dif?cult to a degree that rendersit almost intractableforpracticalpurposes,incrementallanguageprocessing has recently been attracting a steadily growing interest in the spoken language pro cessing community. Its notorious dif?culty can be attributed mainly to two reasons: Due to the inaccessibility of the right context, global optimization criteria are no longer available. This loss must be compensated for by communicating larger search spaces between system components or by introducing appropriate repair mechanisms. In any case, the complexity of the task can easily grow by an order of magnitude or even more. Incrementality is an almost useless feature as long as it remains a local property of individual system components. The advantages of incremental processing can be effectiveonly if all the componentsof a producer consumerchain consistently adhere to the same pattern of temporal behavior.
9783540467618
10.1007/3-540-46761-0 doi
Natural language processing (Computer science).
Artificial intelligence.
Machine theory.
Natural Language Processing (NLP).
Artificial Intelligence.
Formal Languages and Automata Theory.
QA76.9.N38
006.35