Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges
- URL: http://arxiv.org/abs/2409.13594v1
- Date: Fri, 20 Sep 2024 15:49:14 GMT
- Title: Cross-Target Stance Detection: A Survey of Techniques, Datasets, and Challenges
- Authors: Parisa Jamadi Khiabani, Arkaitz Zubiaga,
- Abstract summary: Cross-target stance detection is the task of determining the viewpoint expressed in a text towards a given target.
With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest.
This review paper examines the advancements in cross-target stance detection over the last decade.
- Score: 7.242609314791262
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Stance detection is the task of determining the viewpoint expressed in a text towards a given target. A specific direction within the task focuses on cross-target stance detection, where a model trained on samples pertaining to certain targets is then applied to a new, unseen target. With the increasing need to analyze and mining viewpoints and opinions online, the task has recently seen a significant surge in interest. This review paper examines the advancements in cross-target stance detection over the last decade, highlighting the evolution from basic statistical methods to contemporary neural and LLM-based models. These advancements have led to notable improvements in accuracy and adaptability. Innovative approaches include the use of topic-grouped attention and adversarial learning for zero-shot detection, as well as fine-tuning techniques that enhance model robustness. Additionally, prompt-tuning methods and the integration of external knowledge have further refined model performance. A comprehensive overview of the datasets used for evaluating these models is also provided, offering valuable insights into the progress and challenges in the field. We conclude by highlighting emerging directions of research and by suggesting avenues for future work in the task.
Related papers
- Deep Learning-Based Object Pose Estimation: A Comprehensive Survey [73.74933379151419]
We discuss the recent advances in deep learning-based object pose estimation.
Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks.
arXiv Detail & Related papers (2024-05-13T14:44:22Z) - Few-Shot Object Detection: Research Advances and Challenges [15.916463121997843]
Few-shot object detection (FSOD) combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples.
This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years.
arXiv Detail & Related papers (2024-04-07T03:37:29Z) - Collaborative Knowledge Infusion for Low-resource Stance Detection [83.88515573352795]
Target-related knowledge is often needed to assist stance detection models.
We propose a collaborative knowledge infusion approach for low-resource stance detection tasks.
arXiv Detail & Related papers (2024-03-28T08:32:14Z) - Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for
Advanced Object Detection [55.2480439325792]
We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head.
The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement.
This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
arXiv Detail & Related papers (2023-11-21T19:49:13Z) - Few Shot Semantic Segmentation: a review of methodologies, benchmarks, and open challenges [5.0243930429558885]
Few-Shot Semantic is a novel task in computer vision, which aims at designing models capable of segmenting new semantic classes with only a few examples.
This paper consists of a comprehensive survey of Few-Shot Semantic, tracing its evolution and exploring various model designs.
arXiv Detail & Related papers (2023-04-12T13:07:37Z) - Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey [10.665235711722076]
Oriented object detection is one of the most fundamental and challenging tasks in remote sensing.
Recent years have witnessed remarkable progress in oriented object detection using deep learning techniques.
arXiv Detail & Related papers (2023-02-21T06:31:53Z) - Recent Few-Shot Object Detection Algorithms: A Survey with Performance
Comparison [54.357707168883024]
Few-Shot Object Detection (FSOD) mimics the humans' ability of learning to learn.
FSOD intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes.
We give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols.
arXiv Detail & Related papers (2022-03-27T04:11:28Z) - A Comparative Review of Recent Few-Shot Object Detection Algorithms [0.0]
Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem.
Recent studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions.
arXiv Detail & Related papers (2021-10-30T07:57:11Z) - Deep Learning Schema-based Event Extraction: Literature Review and
Current Trends [60.29289298349322]
Event extraction technology based on deep learning has become a research hotspot.
This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
arXiv Detail & Related papers (2021-07-05T16:32:45Z) - Unsupervised Domain Adaption of Object Detectors: A Survey [87.08473838767235]
Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications.
Learning highly accurate models relies on the availability of datasets with a large number of annotated images.
Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images.
arXiv Detail & Related papers (2021-05-27T23:34:06Z)
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.