, Cet arbre correspond à la phrases "X-name is an Italian restaurant by the river". La représentation arborescente est d'abord convertie dans une chaîne des caractères à l'aide des parenthèses : (<root> <root> ((X-name n :subj) be v : fin ((Italian adj :attr) restaurant n :obj
, Pour pénaliser le système pour les sorties qui manquent ou ajoutent les informations, ils utilisent un LSTM RNN classifieur qui prend en entrée cette phrase prédite et qui détermine s'il y a des omissions ou ajouts des slots par rapport à la représentation sémantique de source, Cette représentation est passé au modèle LSTM RNN du type seq2seq
, Nous avons aussi découvert que la présence de l'intention semble ne pas avoir d'influence sur la prédiction des slots par Tf-seq2seq. En ce qui concerne son mode mot vs caractère, la performance du modèle à caractères est pis sur les intentions mais elle est mieux
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