Do Convolutional Networks need to be Deep for Text Classification ?
Table 1: Accuracy of our proposed models (10 top rows) and of state-of-the-art models from the litterature (13 bottom rows).
['[BOLD] Models', '[BOLD] AGNews', '[BOLD] Yelp Bin', '[BOLD] Yelp Full', '[BOLD] DBPedia', '[BOLD] Yahoo']
(10 rows on top) and compare them with state-of-the-art results (13 rows at the bottom) on 5 corpus and text classification tasks (columns). The bag-of-ngrams model exploits the most frequent word n-grams from the training data (Zhang et al., ngrams TFIDF: fastText: A linear word-level model with a rank constraint and fast loss approximation char-CRNN: Naive Bayes: Kneser-Ney Bayes: A more sophisticated word count-based language model that uses tri-grams and Kneser-Ney smoothing MLP Naive Bayes: Discriminative LSTM: Word-level model with logistic regression on top of a traditional LSTM Generative LSTM-independent comp. : A class-based word language model with no shared parameters across classes Generative LSTM-shared comp.