H93-1016 2 will lead to minimization of sentence recognition error rate . To jointly optimize
H92-1036 also effective for continuous sentence recognition of the RM task . Table 5 gives
C96-2180 procedures of the ALEP system after sentence recognition and before word recognition .
H89-1006 significant improvement in word and sentence recognition accuracy . We have also developed
H92-1016 although the latter achieved better sentence recognition accuracy . Unless otherwise specified
H93-1009 understanding ability actually exceeds its sentence recognition accuracy by 5 % , which suggests
C96-2180 provided : paragraph recognition , sentence recognition and word recognition . The output
C69-2001 grammar itself with Mey 9 respect to sentence recognition ? It has been kno ~ for a long
H94-1037 perfor - mance . The word and sentence recognition error rates for these bookings
A00-1029 around 20 - 30 % improvement in sentence recognition accuracy . We conducted two user
H94-1015 perplexities for test sentences and in sentence recognition rates . As for the proposed model
C96-1049 user-defined tagger between the sentence recognition level and the word recognition
D15-1172 disambiguation task , we extend the sentence recognition model of Siddharth et al. ( 2014
E85-1007 transformations to be used in sentence recognition rather than in generation ( Radford
H89-1010 below we compare the word and sentence recognition error rate when the system is
C82-1006 ... ... . . I ... . I PERCENT OF SENTENCE RECOGNITION ( see text ) 5 . CONCLUSIONS
H93-1034 difficulty using the system . The exact sentence recognition rate by the Verbex machine in
C92-2120 ranked 20 can . didates . While the sentence recognition rate tbr the top candidates remains
H92-1018 recognition error rate of 11.0 % and a sentence recognition error rate of 48.7 % over all
C86-1111 syntactic information during the sentence recognition process are analyzed in detail
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