  Personalized chatbots focus on endowing the chatbots with a consistent
personality to behave like real users and further act as personal assistants.
Previous studies have explored generating implicit user profiles from the
user's dialogue history for building personalized chatbots. However, these
studies only use the response generation loss to train the entire model, thus
it is prone to suffer from the problem of data sparsity. Besides, they
overemphasize the final generated response's quality while ignoring the
correlations and fusions between the user's dialogue history, leading to rough
data representations and performance degradation. To tackle these problems, we
propose a self-supervised learning framework MCP for capturing better
representations from users' dialogue history for personalized chatbots.
Specifically, we apply contrastive sampling methods to leverage the supervised
signals hidden in user dialog history, and generate the pre-training samples
for enhancing the model. We design three pre-training tasks based on three
types of contrastive pairs from user dialogue history, namely response pairs,
sequence augmentation pairs, and user pairs. We pre-train the utterance encoder
and the history encoder towards the contrastive objectives and use these
pre-trained encoders for generating user profiles while personalized response
generation. Experimental results on two real-world datasets show a significant
improvement in our proposed model MCP compared with the existing methods.
