Toward Educator-focused Automated Scoring Systems for Reading and
Writing
- URL: http://arxiv.org/abs/2112.11973v1
- Date: Wed, 22 Dec 2021 15:44:30 GMT
- Title: Toward Educator-focused Automated Scoring Systems for Reading and
Writing
- Authors: Mike Hardy
- Abstract summary: This paper addresses the challenges of data and label availability, authentic and extended writing, domain scoring, prompt and source variety, and transfer learning.
It employs techniques that preserve essay length as an important feature without increasing model training costs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper presents methods for improving automated essay scoring with
techniques that address the computational trade-offs of self-attention and
document length. To make Automated Essay Scoring (AES) more useful to
practitioners, researchers must overcome the challenges of data and label
availability, authentic and extended writing, domain scoring, prompt and source
variety, and transfer learning. This paper addresses these challenges using
neural network models by employing techniques that preserve essay length as an
important feature without increasing model training costs. It introduces
techniques for minimizing classification loss on ordinal labels using
multi-objective learning, capturing semantic information across the entire
essay using sentence embeddings to use transformer architecture across
arbitrarily long documents, the use of such models for transfer learning,
automated hyperparameter generation based on prompt-corpus metadata, and, most
importantly, the use of semantic information to provide meaningful insights
into student reading through analysis of passage-dependent writing resulting in
state-of-the-art results for various essay tasks.
Related papers
- RelevAI-Reviewer: A Benchmark on AI Reviewers for Survey Paper Relevance [0.8089605035945486]
We propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem.
We introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt.
We develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one.
arXiv Detail & Related papers (2024-06-13T06:42:32Z) - The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
Large Language Models (LLMs) are foundational to AI advancements.
LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information.
Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
arXiv Detail & Related papers (2024-03-23T09:26:15Z) - Combatting Human Trafficking in the Cyberspace: A Natural Language
Processing-Based Methodology to Analyze the Language in Online Advertisements [55.2480439325792]
This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques.
We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models.
A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement.
arXiv Detail & Related papers (2023-11-22T02:45:01Z) - Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases.
Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding.
This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
arXiv Detail & Related papers (2023-07-05T20:16:20Z) - Deep Learning Architecture for Automatic Essay Scoring [0.0]
We propose a novel architecture based on recurrent networks (RNN) and convolution neural network (CNN)
In the proposed architecture, the multichannel convolutional layer learns and captures the contextual features of the word n-gram from the word embedding vectors.
Our proposed system achieves significantly higher grading accuracy than other deep learning-based AES systems.
arXiv Detail & Related papers (2022-06-16T14:56:24Z) - Improving Performance of Automated Essay Scoring by using
back-translation essays and adjusted scores [0.0]
We propose a method to increase the number of essay-score pairs using back-translation and score adjustment.
We evaluate the effectiveness of the augmented data using models from prior work.
The performance of the models was improved by using augmented data to train the models.
arXiv Detail & Related papers (2022-03-01T11:05:43Z) - Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or
Something Else? [93.91375268580806]
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms.
Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications.
By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
arXiv Detail & Related papers (2021-11-09T13:30:34Z) - On Learning Text Style Transfer with Direct Rewards [101.97136885111037]
Lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task.
We leverage semantic similarity metrics originally used for fine-tuning neural machine translation models.
Our model provides significant gains in both automatic and human evaluation over strong baselines.
arXiv Detail & Related papers (2020-10-24T04:30:02Z) - Scaling Systematic Literature Reviews with Machine Learning Pipelines [57.82662094602138]
Systematic reviews entail the extraction of data from scientific documents.
We construct a pipeline that automates each of these aspects, and experiment with many human-time vs. system quality trade-offs.
We find that we can get surprising accuracy and generalisability of the whole pipeline system with only 2 weeks of human-expert annotation.
arXiv Detail & Related papers (2020-10-09T16:19:42Z) - Knowledge Guided Metric Learning for Few-Shot Text Classification [22.832467388279873]
We propose to introduce external knowledge into few-shot learning to imitate human knowledge.
Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge.
We demonstrate that our method outperforms the state-of-the-art few-shot text classification models.
arXiv Detail & Related papers (2020-04-04T10:56:26Z)
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.