Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning

• URL: http://arxiv.org/abs/2403.06108v2
• Date: Tue, 9 Apr 2024 16:38:01 GMT
• Title: Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning
• Authors: Kaipeng Wang, Zhi Jing, Yongye Su, Yikun Han,
• Abstract summary: The primary goal of this paper is to address the challenges of detecting subtle emotions in text. The findings offer valuable insights into addressing the challenges of emotion detection in text.
• Score: 1.124958340749622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper delves into enhancing the classification performance on the GoEmotions dataset, a large, manually annotated dataset for emotion detection in text. The primary goal of this paper is to address the challenges of detecting subtle emotions in text, a complex issue in Natural Language Processing (NLP) with significant practical applications. The findings offer valuable insights into addressing the challenges of emotion detection in text and suggest directions for future research, including the potential for a survey paper that synthesizes methods and performances across various datasets in this domain.

Related papers

• Joint-Dataset Learning and Cross-Consistent Regularization for Text-to-Motion Retrieval [4.454835029368504]
  We focus on the recently introduced text-motion retrieval which aim to search for sequences that are most relevant to a natural motion description. Despite recent efforts to explore these promising avenues, a primary challenge remains the insufficient data available to train robust text-motion models. We propose to investigate joint-dataset learning - where we train on multiple text-motion datasets simultaneously. We also introduce a transformer-based motion encoder, called MoT++, which employs the specified-temporal attention to process sequences of skeleton data.
  arXiv  Detail & Related papers  (2024-07-02T09:43:47Z)
• Towards Unified Multi-granularity Text Detection with Interactive Attention [56.79437272168507]
  "Detect Any Text" is an advanced paradigm that unifies scene text detection, layout analysis, and document page detection into a cohesive, end-to-end model. A pivotal innovation in DAT is the across-granularity interactive attention module, which significantly enhances the representation learning of text instances. Tests demonstrate that DAT achieves state-of-the-art performances across a variety of text-related benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T07:25:23Z)
• Learning Generalizable Human Motion Generator with Reinforcement Learning [95.62084727984808]
  Text-driven human motion generation is one of the vital tasks in computer-aided content creation. Existing methods often overfit specific motion expressions in the training data, hindering their ability to generalize. We present textbfInstructMotion, which incorporate the trail and error paradigm in reinforcement learning for generalizable human motion generation.
  arXiv  Detail & Related papers  (2024-05-24T13:29:12Z)
• Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models [23.670143829183104]
  The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. We propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
  arXiv  Detail & Related papers  (2023-11-06T21:34:49Z)
• Boosting Event Extraction with Denoised Structure-to-Text Augmentation [52.21703002404442]
  Event extraction aims to recognize pre-defined event triggers and arguments from texts. Recent data augmentation methods often neglect the problem of grammatical incorrectness. We propose a denoised structure-to-text augmentation framework for event extraction DAEE.
  arXiv  Detail & Related papers  (2023-05-16T16:52:07Z)
• REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection [3.6678641723285446]
  We propose a novel approach for Readers' Emotion Detection from short-text documents using a deep learning model called REDAffectiveLM. We leverage context-specific and affect enriched representations by using a transformer-based pre-trained language model in tandem with affect enriched Bi-LSTM+Attention.
  arXiv  Detail & Related papers  (2023-01-21T19:28:25Z)
• Analysis of Basic Emotions in Texts Based on BERT Vector Representation [0.0]
  The authors present a GAN-type model and the most important stages of its development for the task of emotion recognition in text. We propose an approach for generating a synthetic dataset of all possible emotions combinations based on manually labelled incomplete data.
  arXiv  Detail & Related papers  (2021-01-21T07:11:21Z)
• Target Guided Emotion Aware Chat Machine [58.8346820846765]
  The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post.
  arXiv  Detail & Related papers  (2020-11-15T01:55:37Z)
• Be More with Less: Hypergraph Attention Networks for Inductive Text Classification [56.98218530073927]
  Graph neural networks (GNNs) have received increasing attention in the research community and demonstrated their promising results on this canonical task. Despite the success, their performance could be largely jeopardized in practice since they are unable to capture high-order interaction between words. We propose a principled model -- hypergraph attention networks (HyperGAT) which can obtain more expressive power with less computational consumption for text representation learning.
  arXiv  Detail & Related papers  (2020-11-01T00:21:59Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)
• GoEmotions: A Dataset of Fine-Grained Emotions [16.05879383442812]
  We introduce GoEmotions, the largest manually annotated dataset of 58k English Reddit comments, labeled for 27 emotion categories or Neutral. Our BERT-based model achieves an average F1-score of.46 across our proposed taxonomy, leaving much room for improvement.
  arXiv  Detail & Related papers  (2020-05-01T18:00:02Z)

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.