Fitting Different Interactive Information: Joint Classification of Emotion and Intention

• URL: http://arxiv.org/abs/2501.06215v1
• Date: Sun, 05 Jan 2025 05:23:27 GMT
• Title: Fitting Different Interactive Information: Joint Classification of Emotion and Intention
• Authors: Xinger Li, Zhiqiang Zhong, Bo Huang, Yang Yang,
• Abstract summary: This paper is the first-place solution for ICASSP MEIJU@2025 Track I, which focuses on low-resource multimodal emotion and intention recognition. In this paper, pseudo-label labeling is carried out on the model trained with labeled data, and samples with high confidence and their labels are selected to alleviate the problem of low resources. Under the refined processing data, we achieve the score of 0.5532 in the Test set, and win the championship of the track.
• Score: 6.07748077245386
• License:
• Abstract: This paper is the first-place solution for ICASSP MEIJU@2025 Track I, which focuses on low-resource multimodal emotion and intention recognition. How to effectively utilize a large amount of unlabeled data, while ensuring the mutual promotion of different difficulty levels tasks in the interaction stage, these two points become the key to the competition. In this paper, pseudo-label labeling is carried out on the model trained with labeled data, and samples with high confidence and their labels are selected to alleviate the problem of low resources. At the same time, the characteristic of easy represented ability of intention recognition found in the experiment is used to make mutually promote with emotion recognition under different attention heads, and higher performance of intention recognition is achieved through fusion. Finally, under the refined processing data, we achieve the score of 0.5532 in the Test set, and win the championship of the track.

Related papers

• Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition [24.857301914941175]
  This paper proposes an inconsistency-based active learning approach for cross-task transfer between emotion classification and estimation. Affective norms are utilized as prior knowledge to connect the label spaces of categorical and dimensional emotions. Experiments on within-corpus and cross-corpus transfers demonstrated that cross-task inconsistency could be a very valuable metric in active learning.
  arXiv  Detail & Related papers  (2024-12-02T06:23:46Z)
• Multi-Label Contrastive Learning : A Comprehensive Study [48.81069245141415]
  Multi-label classification has emerged as a key area in both research and industry. Applying contrastive learning to multi-label classification presents unique challenges. We conduct an in-depth study of contrastive learning loss for multi-label classification across diverse settings.
  arXiv  Detail & Related papers  (2024-11-27T20:20:06Z)
• Multi-Label Bayesian Active Learning with Inter-Label Relationships [3.88369051454137]
  We propose a new multi-label active learning strategy to address both challenges. Our method incorporates progressively updated positive and negative correlation matrices to capture co-occurrence and disjoint relationships. Our strategy consistently achieves more reliable and superior performance, compared to several established methods.
  arXiv  Detail & Related papers  (2024-11-26T23:28:54Z)
• Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning [81.83013974171364]
  Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlike semi-supervised learning, one cannot select the most probable label as the pseudo-label in SSMLL due to multiple semantics contained in an instance. We propose a dual-perspective method to generate high-quality pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-26T09:33:53Z)
• JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification [65.268245109828]
  Semi-supervised text classification (SSTC) has gained increasing attention due to its ability to leverage unlabeled data. Existing approaches based on pseudo-labeling suffer from the issues of pseudo-label bias and error accumulation. We propose JointMatch, a holistic approach for SSTC that addresses these challenges by unifying ideas from recent semi-supervised learning.
  arXiv  Detail & Related papers  (2023-10-23T05:43:35Z)
• Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels [6.872072177648135]
  We propose a novel localization algorithm that adapts well-established ground truth estimation methods. Our algorithm also shows superior performance during training on the TexBiG dataset.
  arXiv  Detail & Related papers  (2023-09-18T13:08:44Z)
• Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection [149.23913018423022]
  Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Two-stage self-training methods have achieved significant improvements by self-generating pseudo labels. We propose an enhancement framework by exploiting completeness and uncertainty properties for effective self-training.
  arXiv  Detail & Related papers  (2022-12-08T05:53:53Z)
• Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors [24.365876333182207]
  We propose a novel training loss based on per-utterance Dirichlet prior distributions for verbal emotion recognition. An additional metric is used to evaluate the performance by detecting test utterances with high labelling uncertainty. Experiments with the widely used IEMOCAP dataset demonstrate that the two-branch structure achieves state-of-the-art classification results.
  arXiv  Detail & Related papers  (2022-03-08T23:30:01Z)
• S3: Supervised Self-supervised Learning under Label Noise [53.02249460567745]
  In this paper we address the problem of classification in the presence of label noise. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space. Our method significantly surpasses previous methods on both CIFARCIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.
  arXiv  Detail & Related papers  (2021-11-22T15:49:20Z)
• Dynamic Semantic Matching and Aggregation Network for Few-shot Intent Detection [69.2370349274216]
  Few-shot Intent Detection is challenging due to the scarcity of available annotated utterances. Semantic components are distilled from utterances via multi-head self-attention. Our method provides a comprehensive matching measure to enhance representations of both labeled and unlabeled instances.
  arXiv  Detail & Related papers  (2020-10-06T05:16:38Z)

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.