Search Results for author:
Found 5 papers, 4 papers with code
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling
To tackle these problems, we propose a self-supervised learning framework MCP for capturing better representations from users' dialogue history for personalized chatbots.
Socialformer: Social Network Inspired Long Document Modeling for Document Ranking
In this paper, we propose the model Socialformer, which introduces the characteristics of social networks into designing sparse attention patterns for long document modeling in document ranking.
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.
One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles
Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses.
