Learning to Compose Words into Sentences with Reinforcement Learning
Table 4: Classification accuracy on SNLI dataset.
['Model', 'Acc.', '# params.']
The latent syntax method performs the best. Interestingly, the sequential left to right model is better than the supervised recursive model in our experiments, which contradicts results from Bowman et al. that show 300D-LSTM is worse than 300D-SPINN. A possible explanation is that our left to right model has identical number of parameters with the supervised model due to the inclusion of the tracking LSTM even in the left to right model (the only difference is in the composition order), whereas the models in Bowman et al. Due to the poor performance of the supervised model, semi-supervised training does not help on this dataset, although it does significantly close the gap. Our models underperform state-of-the-art models on this dataset that have almost four times the number of parameters. We only experiment with smaller models since tree-based models with dynamic structures (e.g., our semi-supervised and latent syntax models) take longer to train.