Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks
Zhiliang Tian, Wei Bi, Zihan Zhang, Dongkyu Lee, Yiping Song, Nevin L., Zhang

TL;DR
This paper introduces a few-shot personalized conversation system leveraging social networks, enabling models to generate tailored responses for new users with limited data by learning from social connections.
Contribution
It proposes a meta-learning based PCM with a task aggregator that utilizes social networks to improve personalization for newcomers with scarce conversation data.
Findings
Outperforms baselines in response appropriateness.
Enhances response diversity and consistency.
Effective for new users with limited conversations.
Abstract
Personalized conversation models (PCMs) generate responses according to speaker preferences. Existing personalized conversation tasks typically require models to extract speaker preferences from user descriptions or their conversation histories, which are scarce for newcomers and inactive users. In this paper, we propose a few-shot personalized conversation task with an auxiliary social network. The task requires models to generate personalized responses for a speaker given a few conversations from the speaker and a social network. Existing methods are mainly designed to incorporate descriptions or conversation histories. Those methods can hardly model speakers with so few conversations or connections between speakers. To better cater for newcomers with few resources, we propose a personalized conversation model (PCM) that learns to adapt to new speakers as well as enabling new speakers…
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Taxonomy
TopicsTopic Modeling · Speech and dialogue systems · Speech Recognition and Synthesis
