Efficient Vector Representation for Documents through Corruption
Table 6: Test set results on the SICK semantic relatedness task. The first group of results are from the submission to the 2014 SemEval competition; the second group includes several baseline methods reported in (Tai et al., 2015); the third group are methods based on LSTM reported in (Tai et al., 2015) as well as the skip-thought vectors (Kiros et al., 2015).
['Method', 'Pearson’s  [ITALIC] γ', 'Spearman’s  [ITALIC] ρ', 'MSE']
The performance of Doc2VecC is slightly worse than the LSTM based methods or skip-thought vectors on this dataset, while it significantly outperforms skip-thought vectors on the IMDB movie review dataset (11.70% error rate vs 17.42%). As we hypothesized in previous section, while Doc2VecC is better at handling longer paragraphs, LSTM-based methods are superior for relatively short sentences (of length in the order of 10s). We would like to point out that Doc2VecC is much faster to train and test comparing to skip-thought vectors. It takes less than 2 hours to learn the embeddings on the large book corpus for Doc2VecC on a desktop with Intel i7 2.2Ghz cpu, in comparison to the 2 weeks on GPU required by skip-thought vectors.