Learning to Compose Words into Sentences with Reinforcement Learning
Table 2: Classification accuracy on Stanford Sentiment Treebank dataset. The number of parameters includes word embedding parameters and is our approximation when not reported in previous work.
['Model', 'Acc.', '# params.']
We run each model 3 times (corresponding to three different initialization points) and use the development data to pick the best model. Our results agree with prior work that have shown the benefits of using syntactic parse tree information on this dataset (i.e., supervised recursive model is generally better than sequential models). The best model is the latent syntax model, which is also competitive with results from other work on this dataset. Both the latent and semi-supervised syntax models outperform models with predefined structures, demonstrating the benefit of learning task-specific composition orders.