Enhancing Impression Change Prediction in Speed Dating Simulations Based on Speakers' Personalities

• URL: http://arxiv.org/abs/2502.04706v1
• Date: Fri, 07 Feb 2025 07:18:32 GMT
• Title: Enhancing Impression Change Prediction in Speed Dating Simulations Based on Speakers' Personalities
• Authors: Kazuya Matsuo, Yoko Ishii, Atsushi Otsuka, Ryo Ishii, Hiroaki Sugiyama, Masahiro Mizukami, Tsunehiro Arimoto, Narichika Nomoto, Yoshihide Sato, Tetsuya Yamaguchi,
• Abstract summary: This paper focuses on simulating text dialogues in which impressions between speakers improve during speed dating.<n>We believe that whether an utterance improves a dialogue partner's impression of the speaker may depend on the personalities of both parties.<n>We propose a method that predicts whether an utterance improves a partner's impression of the speaker, considering the personalities.
• Score: 2.1740370446058708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on simulating text dialogues in which impressions between speakers improve during speed dating. This simulation involves selecting an utterance from multiple candidates generated by a text generation model that replicates a specific speaker's utterances, aiming to improve the impression of the speaker. Accurately selecting an utterance that improves the impression is crucial for the simulation. We believe that whether an utterance improves a dialogue partner's impression of the speaker may depend on the personalities of both parties. However, recent methods for utterance selection do not consider the impression per utterance or the personalities. To address this, we propose a method that predicts whether an utterance improves a partner's impression of the speaker, considering the personalities. The evaluation results showed that personalities are useful in predicting impression changes per utterance. Furthermore, we conducted a human evaluation of simulated dialogues using our method. The results showed that it could simulate dialogues more favorably received than those selected without considering personalities.

Related papers

• Aligning Spoken Dialogue Models from User Interactions [55.192134724622235]
  We propose a novel preference alignment framework to improve spoken dialogue models on realtime conversations from user interactions.<n>We create a dataset of more than 150,000 preference pairs from raw multi-turn speech conversations annotated with AI feedback.<n>Our findings shed light on the importance of a well-calibrated balance among various dynamics, crucial for natural real-time speech dialogue systems.
  arXiv  Detail & Related papers  (2025-06-26T16:45:20Z)
• PersonaTAB: Predicting Personality Traits using Textual, Acoustic, and Behavioral Cues in Fully-Duplex Speech Dialogs [36.18860434920165]
  Personality-aware conversation agents are underexplored due to the absence of personality annotations in speech datasets.<n>We propose a pipeline that preprocesses raw audio recordings to create a dialogue dataset annotated with timestamps, response types, and emotion/sentiment labels.<n>We employ an automatic speech recognition (ASR) system to extract transcripts and timestamps, then generate conversation-level annotations.
  arXiv  Detail & Related papers  (2025-05-20T13:41:32Z)
• Towards human-like spoken dialogue generation between AI agents from written dialogue [8.4989907582951]
  This study proposes CHATS - CHatty Agents Text-to-Speech - a discrete token-based system designed to generate spoken dialogues based on written dialogues. Our system can generate speech for both the speaker side and the listener side simultaneously, using only the transcription from the speaker side.
  arXiv  Detail & Related papers  (2023-10-02T11:03:20Z)
• Emotional Listener Portrait: Realistic Listener Motion Simulation in Conversation [50.35367785674921]
  Listener head generation centers on generating non-verbal behaviors of a listener in reference to the information delivered by a speaker. A significant challenge when generating such responses is the non-deterministic nature of fine-grained facial expressions during a conversation. We propose the Emotional Listener Portrait (ELP), which treats each fine-grained facial motion as a composition of several discrete motion-codewords. Our ELP model can not only automatically generate natural and diverse responses toward a given speaker via sampling from the learned distribution but also generate controllable responses with a predetermined attitude.
  arXiv  Detail & Related papers  (2023-09-29T18:18:32Z)
• InferEM: Inferring the Speaker's Intention for Empathetic Dialogue Generation [37.12407597998884]
  Current approaches to empathetic response generation typically encode the entire dialogue history directly. We argue that the last utterance in the dialogue empirically conveys the intention of the speaker. We propose a novel model named InferEM for empathetic response generation.
  arXiv  Detail & Related papers  (2022-12-13T05:12:40Z)
• A Benchmark for Understanding and Generating Dialogue between Characters in Stories [75.29466820496913]
  We present the first study to explore whether machines can understand and generate dialogue in stories. We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition. We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
  arXiv  Detail & Related papers  (2022-09-18T10:19:04Z)
• 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)
• "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations [87.95711406978157]
  This work presents a new benchmark on spoken task-oriented conversations. We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling. Our data set enables speech-based benchmarking of task-oriented dialogue systems.
  arXiv  Detail & Related papers  (2021-09-28T04:51:04Z)
• 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)
• Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue [76.88174667929665]
  A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles. In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely. We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
  arXiv  Detail & Related papers  (2020-09-14T15:07:19Z)
• 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)

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.