Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion

• URL: http://arxiv.org/abs/2106.07857v1
• Date: Tue, 15 Jun 2021 03:21:19 GMT
• Title: Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion
• Authors: Bin Li, Bin Sun (Member, IEEE) and Shutao Li (Fellow, IEEE)
• Abstract summary: 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.
• Score: 3.5433229509828155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating personalized responses is one of the major challenges in natural human-robot interaction. Current researches in this field mainly focus on generating responses consistent with the robot's pre-assigned persona, while ignoring the user's persona. Such responses may be inappropriate or even offensive, which may lead to the bad user experience. Therefore, we propose a bilateral personalized dialogue generation (BPDG) method with dynamic persona-aware fusion via multi-task transfer learning to generate responses consistent with both personas. The proposed method aims to accomplish three learning tasks: 1) an encoder is trained with dialogue utterances added with corresponded personalized attributes and relative position (language model task), 2) a dynamic persona-aware fusion module predicts the persona presence to adaptively fuse the contextual and bilateral personas encodings (persona prediction task) and 3) a decoder generates natural, fluent and personalized responses (dialogue generation task). To make the generated responses more personalized and bilateral persona-consistent, the Conditional Mutual Information Maximum (CMIM) criterion is adopted to select the final response from the generated candidates. The experimental results show that the proposed method outperforms several state-of-the-art methods in terms of both automatic and manual evaluations.

Related papers

• Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences [53.353022588751585]
  We present Promptable Behaviors, a novel framework that facilitates efficient personalization of robotic agents to diverse human preferences. We introduce three distinct methods to infer human preferences by leveraging different types of interactions. We evaluate the proposed method in personalized object-goal navigation and flee navigation tasks in ProcTHOR and RoboTHOR.
  arXiv  Detail & Related papers  (2023-12-14T21:00:56Z)
• Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization [20.722098595079945]
  We propose a model-agnostic framework, ORder Insensitive Generation (ORIG), to mitigate the order sensitivity problem. Experiments on the Persona-Chat dataset justify the effectiveness and superiority of our method.
  arXiv  Detail & Related papers  (2023-05-22T07:24:29Z)
• EM Pre-training for Multi-party Dialogue Response Generation [86.25289241604199]
  In multi-party dialogues, the addressee of a response utterance should be specified before it is generated. We propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels.
  arXiv  Detail & Related papers  (2023-05-21T09:22:41Z)
• 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)
• Towards Building a Personalized Dialogue Generator via Implicit User Persona Detection [0.0]
  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.
  arXiv  Detail & Related papers  (2022-04-15T08:12:10Z)
• Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots [51.091235903442715]
  This paper makes an attempt to explore the impact of utilizing personas that describe either self or partner speakers on the task of response selection. Four persona fusion strategies are designed, which assume personas interact with contexts or responses in different ways. Empirical studies on the Persona-Chat dataset show that the partner personas can improve the accuracy of response selection.
  arXiv  Detail & Related papers  (2021-05-19T10:32:30Z)
• Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots [62.295373408415365]
  We propose a personalized hybrid matching network (PHMN) for context-response matching. Our contributions are two-fold: 1) our model extracts personalized wording behaviors from user-specific dialogue history as extra matching information. We evaluate our model on two large datasets with user identification, i.e., personalized dialogue Corpus Ubuntu (P- Ubuntu) and personalized Weibo dataset (P-Weibo)
  arXiv  Detail & Related papers  (2021-03-17T09:42:11Z)
• 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.