Generating Sentences from a Continuous Space
Table 10: Results for TREC Question Classification.
['[BOLD] Method', '[BOLD] Accuracy']
Here, ae is a plain sequence autoencoder. We were not able to make the vae codes perform better than cbow in this case, but they did outperform features from the sequence autoencoder. Skip-thought performed quite well, possibly because the skip-thought training objective of next sentence prediction is well aligned to this task: it essentially trains the model to generate sentences that address implicit open questions from the narrative of the book. Combining the two representations did not give any additional performance gain over the base skip-thought model.