DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning
Table 3: Top@3 performance of utterance prediction task.
['Model', 'BLEU@1', 'BLEU@2', 'BLEU@3', 'BLEU@4', 'BLEU@4(cumu)']
This also illustrates that the act sequence information, the DialogAct2Vec Autoencoder and multi-task learning can jointly contribute to the performance. Seq2Seq with Attention model uses a generative model for language generation, so the utterance quality is significantly lower than other retrieval based methods. Although the pipeline based HCNs has comparable performance with the SingleMTRL(Utt) model in terms of BLEU@4(cumu), the later model requires much less handcrafted work during system design. This experiment indirectly suggests that the task of predicting the next act can also help to better predict the next utterance.