Towards Building a Personalized Dialogue Generator via Implicit User Persona Detection

• URL: http://arxiv.org/abs/2204.07372v1
• Date: Fri, 15 Apr 2022 08:12:10 GMT
• Title: Towards Building a Personalized Dialogue Generator via Implicit User Persona Detection
• Authors: Itsugun Cho, Dongyang Wang, Ryota Takahashi and Hiroaki Saito
• Abstract summary: We consider high-quality transmission is essentially built based on apprehending the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting implicit user persona.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current works in the generation of personalized dialogue primarily contribute to the agent avoiding contradictory persona and driving the response more informative. However, we found that the generated responses from these models are mostly self-centered with little care for the other party since they ignore the user's persona. Moreover, we consider high-quality transmission is essentially built based on apprehending the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting implicit user persona. Because it's difficult to collect a large number of personas for each user, we attempt to model the user's potential persona and its representation from the dialogue absence of any external information. Perception variable and fader variable are conceived utilizing Conditional Variational Inference. The two latent variables simulate the process of people being aware of the other party's persona and producing the corresponding expression in conversation. Finally, Posterior-discriminated Regularization is presented to enhance the training procedure. Empirical studies demonstrate that compared with the state-of-the-art methods, ours is more concerned with the user's persona and outperforms in evaluations.

Related papers

• Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems [103.416202777731]
  We study "persona biases", which we define to be the sensitivity of dialogue models' harmful behaviors contingent upon the personas they adopt. We categorize persona biases into biases in harmful expression and harmful agreement, and establish a comprehensive evaluation framework to measure persona biases in five aspects: Offensiveness, Toxic Continuation, Regard, Stereotype Agreement, and Toxic Agreement.
  arXiv  Detail & Related papers  (2023-10-08T21:03:18Z)
• Improving Personality Consistency in Conversation by Persona Extending [22.124187337032946]
  We propose a novel retrieval-to-prediction paradigm consisting of two subcomponents, namely, Persona Retrieval Model (PRM) and Posterior-scored Transformer (PS-Transformer) Our proposed model yields considerable improvements in both automatic metrics and human evaluations.
  arXiv  Detail & Related papers  (2022-08-23T09:00:58Z)
• Understanding How People Rate Their Conversations [73.17730062864314]
  We conduct a study to better understand how people rate their interactions with conversational agents. We focus on agreeableness and extraversion as variables that may explain variation in ratings.
  arXiv  Detail & Related papers  (2022-06-01T00:45:32Z)
• Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation [57.73547958927826]
  We propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator.
  arXiv  Detail & Related papers  (2022-04-18T02:02:56Z)
• Learning to Predict Persona Information forDialogue Personalization without Explicit Persona Description [10.17868476063421]
  We propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses. A trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.
  arXiv  Detail & Related papers  (2021-11-30T03:19:24Z)
• DLVGen: A Dual Latent Variable Approach to Personalized Dialogue Generation [28.721411816698563]
  We propose a Dual Latent Variable Generator (DLVGen) capable of generating personalized dialogue. Unlike prior work, DLVGen models the latent distribution over potential responses as well as the latent distribution over the agent's potential persona. Empirical results show that DLVGen is capable of generating diverse responses which accurately incorporate the agent's persona.
  arXiv  Detail & Related papers  (2021-11-22T17:21:21Z)
• Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion [3.5433229509828155]
  We propose a bilateral personalized dialogue generation (BPDG) method with dynamic persona-aware fusion via multi-task transfer learning. The experimental results show that the proposed method outperforms several state-of-the-art methods in terms of both automatic and manual evaluations.
  arXiv  Detail & Related papers  (2021-06-15T03:21:19Z)
• Revealing Persona Biases in Dialogue Systems [64.96908171646808]
  We present the first large-scale study on persona biases in dialogue systems. We conduct analyses on personas of different social classes, sexual orientations, races, and genders. In our studies of the Blender and DialoGPT dialogue systems, we show that the choice of personas can affect the degree of harms in generated responses.
  arXiv  Detail & Related papers  (2021-04-18T05:44:41Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)
• A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation [52.743311026230714]
  Persona Exploration and Exploitation (PEE) is able to extend the predefined user persona description with semantically correlated content. PEE consists of two main modules: persona exploration and persona exploitation. Our approach outperforms state-of-the-art baselines in terms of both automatic and human evaluations.
  arXiv  Detail & Related papers  (2020-02-06T08:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.