SOTAVerified

Personalized Response Generation via Generative Split Memory Network

2021-06-01NAACL 2021Code Available1· sign in to hype

Yuwei Wu, Xuezhe Ma, Diyi Yang

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

Despite the impressive successes of generation and dialogue systems, how to endow a text generation system with particular personality traits to deliver more personalized responses remains under-investigated. In this work, we look at how to generate personalized responses for questions on Reddit by utilizing personalized user profiles and posting histories. Specifically, we release an open-domain single-turn dialog dataset made up of 1.5M conversation pairs together with 300k profiles of users and related comments. We then propose a memory network to generate personalized responses in dialogue that utilizes a novel mechanism of splitting memories: one for user profile meta attributes and the other for user-generated information like comment histories. Experimental results show the quantitative and qualitative improvements of our simple split memory network model over the state-of-the-art response generation baselines.

Tasks

Reproductions