| dc.description.abstract | In recent years, many studies have proposed many kinds of neural network-based dialogue systems, but analog dialogue is still one of the most difficult challenges in the field of dialogue generation. Most of the dialogue systems and related research still use the Seq2Seq model based on RNN architecture. In addition, Transformer performs much better in the field of Neural Machine Translation (NMT) than RNN-based Seq2Seq models, but few studies have evaluated and compared RNN-based Seq2Seq models and Transformer models in the field of dialog generation, and the way in which the dialog generation model is evaluated is still not able to use a single evaluation benchmark to evaluate the model′s generated response.
Therefore, this study will use RNN-based Seq2Seq model and Transformer model, and models were trained using two movie subtitles and conversation-related data sets, Cornell Movie-Dialog Corpus and OpenSubtitles Corpus. Due to the nature of the dataset, this study will also focus on the open-domain dialogue model and use a variety of quantitative analysis indicators and qualitative analysis to verify the suitability of the two model architectures in the open-domain dialog generation domain. And explore the interdependence and reliability of the various methods of dialogue evaluation.
From the results of quantitative analysis and qualitative analysis, the RNN-based Seq2Seq model is suitable for short answers and conservative responses. The overall quality and predictive power of the Transformer model is higher than that of the RNN-based Seq2Seq model, and it is good at answering simple inference questions and generating longer responses than the latter. In this study, we find the dependence of various evaluation indicators. It is expected that in the future research, the Transformer model will be introduced and replaced with the RNN-based Seq2Seq model in the architecture and tasks of different models, and the process of this research evaluation will be introduced into future research.
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