  There is a growing interest in improving the conversational ability of models
by filtering the raw dialogue corpora. Previous filtering strategies usually
rely on a scoring method to assess and discard samples from one perspective,
enabling the model to enhance the corresponding dialogue attributes (e.g.,
consistency) more easily. However, the discarded samples may obtain high scores
in other perspectives and can provide regularization effects on the model
learning, which causes the performance improvement to be sensitive to the
filtering ratio. In this work, we propose a multi-view attribute-enhanced
dialogue learning framework that strengthens the attribute-related features
more robustly and comprehensively. Instead of filtering the raw dataset to
train the model, our framework first pre-trains the model on the raw dataset
and then fine-tunes it through adapters on the selected sub-sets, which also
enhances certain attributes of responses but without suffering from the
problems mentioned above. Considering the variety of the dialogue attribute, we
further design a multi-view enhancement mechanism, including multi-view
selection and inter-view fusion. It groups the high-quality samples from
multiple perspectives, respectively, and enhances different attributes of
responses with the corresponding sample sets and adapters, keeping knowledge
independent and allowing flexible integration. Empirical results and analysis
show that our framework can improve the performance significantly in terms of
enhancing dialogue attributes and fusing view-specific knowledge.
