Evaluations on multiple text classification tasks show that ProSeqo significantly improved accuracy compared to state-of-the-art on-device neural network SGNN (Ravi and Kozareva, 2018) on short text with +3.4% for MRDA, +4.5% on SNIPS, +5.3% on SWDA and +8.9% on ATIS; and long documents with +23% on Amazon product reviews, +33.9% on AG news, +35.9% on Y!A.
for ATIS and SNIPs, the most recent state-of-art approaches use joint intent and slot prediction model (Hakkani-Tur et al., 2016;Liu and Lane, 2016;Goo et al., 2018), where the slot model recognizes the enti- AG Y!A AMZN ProSeqo (our on-device model) 91.5 72.4 62.3 SGNN (Ravi and Kozareva, 2018)(on-device) 57.6 36.5 39.3 fastText-full (Joulin et al., 2016) 92.5 72.3 60.2 CharCNNLargeWithThesau.