The Lifted Matrix-Space Model for Semantic Composition
Table 6: Syntactic category classification accuracies (%) on SNLI development set, classified using the tags introduced in Bowman et al. (2015).
['[BOLD] Model', '[BOLD] 3-way  [BOLD] Train', '[BOLD] 3-way  [BOLD] Test', '[BOLD] 19-way  [BOLD] Train', '[BOLD] 19-way  [BOLD] Test']
As a baseline, we train a bag-of-words (BOW) model which produces the hidden state of a given phrase by summing the GloVe embeddings of the words of the phrase. We train and test on the hidden states produced by BOW as well. The hidden state representations produced by LMS-LSTM yield the best results on both 3-way and 19-way classification tasks. Comparing LMS-LSTM and TreeLSTM representations, we see a 5.1% gain on the 3-way classification and a 5.5% gain on the 19-way classification.