Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection

• URL: http://arxiv.org/abs/2106.01071v1
• Date: Wed, 2 Jun 2021 10:57:44 GMT
• Title: Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection
• Authors: Lixing Zhu and Gabriele Pergola and Lin Gui and Deyu Zhou and Yulan He
• Abstract summary: We propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction.
• Score: 24.67719513300731
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.

Related papers

• Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer [78.35816158511523]
  We present a single-stage emotion recognition approach, employing a Decoupled Subject-Context Transformer (DSCT) for simultaneous subject localization and emotion classification. We evaluate our single-stage framework on two widely used context-aware emotion recognition datasets, CAER-S and EMOTIC.
  arXiv  Detail & Related papers  (2024-04-26T07:30:32Z)
• 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)
• Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
  We propose a Dynamic Causal Disentanglement Model based on hidden variable separation. This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions. Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
  arXiv  Detail & Related papers  (2023-09-13T12:58:09Z)
• Multi-Granularity Prompts for Topic Shift Detection in Dialogue [13.739991183173494]
  The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance. We take a prompt-based approach to fully extract topic information from dialogues at multiple-granularity, i.e., label, turn, and topic.
  arXiv  Detail & Related papers  (2023-05-23T12:35:49Z)
• Context-Dependent Embedding Utterance Representations for Emotion Recognition in Conversations [1.8126187844654875]
  We approach Emotion Recognition in Conversations leveraging the conversational context. We propose context-dependent embedding representations of each utterance. The effectiveness of our approach is validated on the open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset.
  arXiv  Detail & Related papers  (2023-04-17T12:37:57Z)
• Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue [54.98184262897166]
  We investigate how the order of the knowledge set can influence autoregressive dialogue systems' responses. We propose a simple and novel technique to alleviate the order effect by modifying the position embeddings of knowledge input.
  arXiv  Detail & Related papers  (2023-02-12T10:13:00Z)
• deep learning of segment-level feature representation for speech emotion recognition in conversations [9.432208348863336]
  We propose a conversational speech emotion recognition method to deal with capturing attentive contextual dependency and speaker-sensitive interactions. First, we use a pretrained VGGish model to extract segment-based audio representation in individual utterances. Second, an attentive bi-directional recurrent unit (GRU) models contextual-sensitive information and explores intra- and inter-speaker dependencies jointly.
  arXiv  Detail & Related papers  (2023-02-05T16:15:46Z)
• AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification [0.29360071145551075]
  The proposed model of Advanced Contextual Feature Extraction (AdCOFE) addresses these issues. Experiments on the Emotion recognition in conversations dataset show that AdCOFE is beneficial in capturing emotions in conversations.
  arXiv  Detail & Related papers  (2021-04-09T17:58:19Z)
• COSMIC: COmmonSense knowledge for eMotion Identification in Conversations [95.71018134363976]
  We propose COSMIC, a new framework that incorporates different elements of commonsense such as mental states, events, and causal relations. We show that COSMIC achieves new state-of-the-art results for emotion recognition on four different benchmark conversational datasets.
  arXiv  Detail & Related papers  (2020-10-06T15:09:38Z)
• Topic-Aware Multi-turn Dialogue Modeling [91.52820664879432]
  This paper presents a novel solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network.
  arXiv  Detail & Related papers  (2020-09-26T08:43:06Z)

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.