Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers

• URL: http://arxiv.org/abs/2204.03503v1
• Date: Fri, 11 Mar 2022 13:47:08 GMT
• Title: Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers
• Authors: Stefan Haller, Adina Aldea, Christin Seifert, Nicola Strisciuglio
• Abstract summary: Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG.
• Score: 5.968260239320591
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG, of which we survey the recent research advancements. We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches. In particular, we focus our analysis on the transition from hand engineered features to representation learning approaches, which learn representative features for the task at hand automatically from large corpora of data. We structure our analysis of deep learning methods along three categories: word embeddings, sequential models, and attention-based methods. Deep learning impacted ASAG differently than other fields of NLP, as we noticed that the learned representations alone do not contribute to achieve the best results, but they rather show to work in a complementary way with hand-engineered features. The best performance are indeed achieved by methods that combine the carefully hand-engineered features with the power of the semantic descriptions provided by the latest models, like transformers architectures. We identify challenges and provide an outlook on research direction that can be addressed in the future

Related papers

• Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications [17.571124565519263]
  Book covers state-of-the-art advancements in machine learning and deep learning. Focuses on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. Book also delves into the integration of artificial intelligence (AI) techniques and large language models for enhanced object detection.
  arXiv  Detail & Related papers  (2024-10-21T02:10:49Z)
• ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis [5.012314384895538]
  We argue that not all of the essential features of what makes an operation important can be accurately captured mathematically using rewards. We propose an AutoEDA model trained through imitation learning from expert EDA sessions, bypassing the need for manually defined interestingness measures. Our method outperforms the existing state-of-the-art end-to-end EDA approach on benchmarks by upto 3x, showing strong performance and generalization.
  arXiv  Detail & Related papers  (2024-10-15T04:56:13Z)
• From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
  Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Large foundation models, such as large language models, have revolutionized various natural language processing tasks. This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
  arXiv  Detail & Related papers  (2024-03-18T17:57:09Z)
• A Probabilistic Model Behind Self-Supervised Learning [53.64989127914936]
  In self-supervised learning (SSL), representations are learned via an auxiliary task without annotated labels. We present a generative latent variable model for self-supervised learning. We show that several families of discriminative SSL, including contrastive methods, induce a comparable distribution over representations.
  arXiv  Detail & Related papers  (2024-02-02T13:31:17Z)
• Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images [78.56114271538061]
  We introduce an explicit point-based human reconstruction framework called HaP. Our approach is featured by fully-explicit point cloud estimation, manipulation, generation, and refinement in the 3D geometric space. Our results may indicate a paradigm rollback to the fully-explicit and geometry-centric algorithm design.
  arXiv  Detail & Related papers  (2023-11-06T05:52:29Z)
• SINC: Self-Supervised In-Context Learning for Vision-Language Tasks [64.44336003123102]
  We propose a framework to enable in-context learning in large language models. A meta-model can learn on self-supervised prompts consisting of tailored demonstrations. Experiments show that SINC outperforms gradient-based methods in various vision-language tasks.
  arXiv  Detail & Related papers  (2023-07-15T08:33:08Z)
• Self-supervised on Graphs: Contrastive, Generative,or Predictive [25.679620842010422]
  Self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks. We divide existing graph SSL methods into three categories: contrastive, generative, and predictive. We also summarize the commonly used datasets, evaluation metrics, downstream tasks, and open-source implementations of various algorithms.
  arXiv  Detail & Related papers  (2021-05-16T03:30:03Z)
• A Survey on Contrastive Self-supervised Learning [0.0]
  Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. Contrastive learning has recently become a dominant component in self-supervised learning methods for computer vision, natural language processing (NLP), and other domains. This paper provides an extensive review of self-supervised methods that follow the contrastive approach.
  arXiv  Detail & Related papers  (2020-10-31T21:05:04Z)
• Concept Learners for Few-Shot Learning [76.08585517480807]
  We propose COMET, a meta-learning method that improves generalization ability by learning to learn along human-interpretable concept dimensions. We evaluate our model on few-shot tasks from diverse domains, including fine-grained image classification, document categorization and cell type annotation.
  arXiv  Detail & Related papers  (2020-07-14T22:04:17Z)
• A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
  We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA. This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn) Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-04-04T14:59:32Z)
• Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends [10.176394550114411]
  The main contribution of this paper is to present an up-to-date and comprehensive survey on different techniques of speech representation learning. Recent reviews in speech have been conducted for ASR, SR, and SER, however, none of these has focused on the representation learning from speech.
  arXiv  Detail & Related papers  (2020-01-02T10:12:23Z)

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.