DialogAct2Vec: Towards End-to-End Dialogue Agent by Multi-Task Representation Learning
Table 2: Performance of act prediction task.
['Model', 'Micro-F1', 'Macro-F1']
We can find that both the act sequence information and the DialogAct2Vec Autoencoder are greatly helpful to improve the performance, compared between MTRL and other baselines. Seeing from BiLSTM-CRF’s performance, it seems only encoding historical information or act sequence information does not guarantee the best results on Micro-F1. However, combining all the positive components with multi-task learning, like our model, can achieve the state-of-the-art performance, which means the other task of utterance prediction also contributes positively to this task of act prediction. 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.