An efficient framework for learning sentence representations
Table 8: Comparison (Pearson score) of sentence representations on Semantic Textual Similarity (STS14) tasks. SDAE, CBOW, Skipgram and FastSent are from Hill et al. (2016). The other baselines are Skip-Thoughts (Kiros et al., 2015) and Siamese CBOW (Kenter et al., 2016). QT (RNN) and QT (BoW) are our models trained with RNN and BoW encoders, respectively.
['Model', 'News', 'Forum', 'WordNet', 'Tweets', 'Images', 'Headlines', 'Overall']
Methods are categorized as sequence models and bag-of-words models. Our RNN-based encoder performs strongly compared to other sequence encoders. Bag-of-words models are known to perform strongly in this task as they are better able to encode word identity information. Our BoW variation performs comparably to prior BoW based models.