Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation

• URL: http://arxiv.org/abs/2404.11141v1
• Date: Wed, 17 Apr 2024 07:36:40 GMT
• Title: Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation
• Authors: Barbara Gendron, Gaël Guibon,
• Abstract summary: We propose a way to model the conversational context that we incorporate into a metric learning training strategy. Using metric learning through a Siamese Network architecture, we achieve 57.71 in macro F1 score for emotion classification in conversation.
• Score: 1.1095648823126325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The advent of deep learning models has made a considerable contribution to the achievement of Emotion Recognition in Conversation (ERC). However, this task still remains an important challenge due to the plurality and subjectivity of human emotions. Previous work on ERC provides predictive models using mostly graph-based conversation representations. In this work, we propose a way to model the conversational context that we incorporate into a metric learning training strategy, with a two-step process. This allows us to perform ERC in a flexible classification scenario and to end up with a lightweight yet efficient model. Using metric learning through a Siamese Network architecture, we achieve 57.71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work. This state-of-the-art result is promising regarding the use of metric learning for emotion recognition, yet perfectible compared to the microF1 score obtained.

Related papers

• 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)
• EmotionIC: emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation [34.24557248359872]
  We propose an emotional inertia and contagion-driven dependency modeling approach (EmotionIC) for ERC task. Our EmotionIC consists of three main components, i.e., Identity Masked Multi-Head Attention (IMMHA), Dialogue-based Gated Recurrent Unit (DiaGRU) and Skip-chain Conditional Random Field (SkipCRF) Experimental results show that our method can significantly outperform the state-of-the-art models on four benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-20T13:58:35Z)
• Deep Emotion Recognition in Textual Conversations: A Survey [0.8602553195689513]
  New applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker, and emotion dynamics modelling, to interpreting common sense expressions. This survey emphasizes the advantage of leveraging techniques to address unbalanced data.
  arXiv  Detail & Related papers  (2022-11-16T19:42:31Z)
• Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation [14.651642872901496]
  Emotion recognition in conversation (ERC) aims to detect the emotion for each utterance in a given conversation. Recent ERC models have leveraged pre-trained language models (PLMs) with the paradigm of pre-training and fine-tuning to obtain good performance. We propose a novel ERC model CISPER with the new paradigm of prompt and language model (LM) tuning.
  arXiv  Detail & Related papers  (2022-07-27T02:34:05Z)
• M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation [1.3864478040954673]
  We propose a Multi-modal Fusion Network (M2FNet) that extracts emotion-relevant features from visual, audio, and text modality. It employs a multi-head attention-based fusion mechanism to combine emotion-rich latent representations of the input data. The proposed feature extractor is trained with a novel adaptive margin-based triplet loss function to learn emotion-relevant features from the audio and visual data.
  arXiv  Detail & Related papers  (2022-06-05T14:18:58Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Hybrid Curriculum Learning for Emotion Recognition in Conversation [10.912215835115063]
  Our framework consists of two curricula: (1) conversation-level curriculum (CC); and (2) utterance-level curriculum (UC) With the proposed model-agnostic hybrid curriculum learning strategy, we observe significant performance boosts over a wide range of existing ERC models.
  arXiv  Detail & Related papers  (2021-12-22T08:02:58Z)
• Sequence-level self-learning with multiple hypotheses [53.04725240411895]
  We develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR) In contrast to conventional unsupervised learning approaches, we adopt the emphmulti-task learning (MTL) framework. Our experiment results show that our method can reduce the WER on the British speech data from 14.55% to 10.36% compared to the baseline model trained with the US English data only.
  arXiv  Detail & Related papers  (2021-12-10T20:47:58Z)
• MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition [118.73025093045652]
  We propose a pre-training model textbfMEmoBERT for multimodal emotion recognition. Unlike the conventional "pre-train, finetune" paradigm, we propose a prompt-based method that reformulates the downstream emotion classification task as a masked text prediction. Our proposed MEmoBERT significantly enhances emotion recognition performance.
  arXiv  Detail & Related papers  (2021-10-27T09:57:00Z)
• EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model [56.75775793011719]
  We introduce and publicly release a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation. Unlike those models which need additional reference audio as input, our model could predict emotion labels just from the input text and generate more expressive speech conditioned on the emotion embedding. In the experiment phase, we first validate the effectiveness of our dataset by an emotion classification task. Then we train our model on the proposed dataset and conduct a series of subjective evaluations.
  arXiv  Detail & Related papers  (2021-06-17T08:34:21Z)
• Speech Command Recognition in Computationally Constrained Environments with a Quadratic Self-organized Operational Layer [92.37382674655942]
  We propose a network layer to enhance the speech command recognition capability of a lightweight network. The employed method borrows the ideas of Taylor expansion and quadratic forms to construct a better representation of features in both input and hidden layers. This richer representation results in recognition accuracy improvement as shown by extensive experiments on Google speech commands (GSC) and synthetic speech commands (SSC) datasets.
  arXiv  Detail & Related papers  (2020-11-23T14:40:18Z)

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.