Compositional Sentence Representation from Character within Large Context Text
Table 7: Performance comparison with other methods for dialogue act classification on SWBD-DAMSL.
['Method', 'Test err. (%)']
Our approach outperforms the other benchmarks, achieving 22.7% classification error rate on the test set. We conjecture that the improvement demonstrated by our model is due to two factors. First, our model build word representations from constituent characters and so suffers less from the data sparsity problem when confronted with rare words, and second the hierarchy-wise language learning method alleviates optimization difficulties of the deep hierarchical recurrent network. To the best our knowledge, our model achieves the state-of-the-art performance of dialogue act classification on the SWBD-DAMSL database.