Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation

• URL: http://arxiv.org/abs/2012.04882v1
• Date: Wed, 9 Dec 2020 06:09:31 GMT
• Title: Infusing Multi-Source Knowledge with Heterogeneous Graph Neural Network for Emotional Conversation Generation
• Authors: Yunlong Liang, Fandong Meng, Ying Zhang, Jinan Xu, Yufeng Chen and Jie Zhou
• Abstract summary: In a real-world conversation, we instinctively perceive emotions from multi-source information. We propose a heterogeneous graph-based model for emotional conversation generation. Experimental results show that our model can effectively perceive emotions from multi-source knowledge.
• Score: 25.808037796936766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The success of emotional conversation systems depends on sufficient perception and appropriate expression of emotions. In a real-world conversation, we firstly instinctively perceive emotions from multi-source information, including the emotion flow of dialogue history, facial expressions, and personalities of speakers, and then express suitable emotions according to our personalities, but these multiple types of information are insufficiently exploited in emotional conversation fields. To address this issue, we propose a heterogeneous graph-based model for emotional conversation generation. Specifically, we design a Heterogeneous Graph-Based Encoder to represent the conversation content (i.e., the dialogue history, its emotion flow, facial expressions, and speakers' personalities) with a heterogeneous graph neural network, and then predict suitable emotions for feedback. After that, we employ an Emotion-Personality-Aware Decoder to generate a response not only relevant to the conversation context but also with appropriate emotions, by taking the encoded graph representations, the predicted emotions from the encoder and the personality of the current speaker as inputs. Experimental results show that our model can effectively perceive emotions from multi-source knowledge and generate a satisfactory response, which significantly outperforms previous state-of-the-art models.

Related papers

• Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
  We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system. We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
  arXiv  Detail & Related papers  (2024-04-03T08:48:50Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• E-CORE: Emotion Correlation Enhanced Empathetic Dialogue Generation [33.57399405783864]
  We propose a novel emotion correlation enhanced empathetic dialogue generation framework. Specifically, a multi-resolution emotion graph is devised to capture context-based emotion interactions. We then propose an emotion correlation enhanced decoder, with a novel correlation-aware aggregation and soft/hard strategy.
  arXiv  Detail & Related papers  (2023-11-25T12:47:39Z)
• In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis [15.16865739526702]
  We introduce a methodology that uses self-supervised networks to disentangle the lexical, speaker, and emotional content of the utterance. We then use a HiFiGAN vocoder to resynthesise the disentangled representations to a speech signal of the targeted emotion. Results reveal that the proposed approach is aptly conditioned on the emotional content of input speech and is capable of synthesising natural-sounding speech for a target emotion.
  arXiv  Detail & Related papers  (2023-06-02T21:02:51Z)
• Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection [47.60224978460442]
  We propose a Serial and Emotion-Knowledge interaction (SEEK) method for empathetic dialogue generation. We use a fine-grained encoding strategy which is more sensitive to the emotion dynamics (emotion flow) in the conversations to predict the emotion-intent characteristic of response. Besides, we design a novel framework to model the interaction between knowledge and emotion to generate more sensible response.
  arXiv  Detail & Related papers  (2022-10-21T03:51:18Z)
• Emotion Intensity and its Control for Emotional Voice Conversion [77.05097999561298]
  Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding.
  arXiv  Detail & Related papers  (2022-01-10T02:11:25Z)
• Automatically Select Emotion for Response via Personality-affected Emotion Transition [0.0]
  dialog systems should be capable to automatically select appropriate emotions for responses like humans. Most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked. We equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion transition of humans in conversation.
  arXiv  Detail & Related papers  (2021-06-30T07:00:42Z)
• Emotion-aware Chat Machine: Automatic Emotional Response Generation for Human-like Emotional Interaction [55.47134146639492]
  This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post. Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
  arXiv  Detail & Related papers  (2021-06-06T06:26:15Z)
• Enhancing Cognitive Models of Emotions with Representation Learning [58.2386408470585]
  We present a novel deep learning-based framework to generate embedding representations of fine-grained emotions. Our framework integrates a contextualized embedding encoder with a multi-head probing model. Our model is evaluated on the Empathetic Dialogue dataset and shows the state-of-the-art result for classifying 32 emotions.
  arXiv  Detail & Related papers  (2021-04-20T16:55:15Z)
• Knowledge Bridging for Empathetic Dialogue Generation [52.39868458154947]
  Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. We propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation.
  arXiv  Detail & Related papers  (2020-09-21T09:21:52Z)

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.