N04-2006 errors affected performance in article generation . 4.5 Article Generation We trained
N04-2006 . These sets were used in the article generation experiments . TRAINGEN : This
N04-2006 83.6 % accuracy . 3 Approach The article generation task constitutes one component
C90-2046 sentence have an effect on the article generation in different languages . Head-Specifier
N04-2006 disambiguate the arti - cles . Article generation , however , clearly depends on
W00-0708 an important role in automated article generation . 1 Introduction Article choice
P06-1031 method is the best performing article generation method . " she is the good student
N04-2006 accordingly . 5 Future Work 5.1 Article Generation We would like to improve the
P15-1084 techniques . However , recent work on article generation ( Banerjee et al. , 2014 ) has
N04-2006 TRAINGEN , then performed the article generation task on all test sets . Table
N04-2006 applied the log-linear model on the article generation task , using features drawn from
N04-2006 weights . 4.2 Training Sets for Article Generation We created three additional training
N09-1059 such as MT candidate selection , article generation , and countability detection
P15-1043 content models for scientific survey article generation by annotating sentences from
P15-1043 content models for scientific survey article generation containing 3,425 sentences from
P15-2023 listed in Table 5 incorporated article generation and demonstrated its positive
P15-1043 introduction sentences for survey article generation as opposed to previous work .
D12-1097 anaphora resolution for Japanese and article generation for English during translation
D13-1139 presicions come from postediting ( article generation ) . In table 4 , we show the
N04-2006 missing articles . On the one hand , article generation performance degraded significantly
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