Automated Essay Scoring (AES) has been quite popular and is being widely used. However, lack of appropriate methodology for rating nonnative English speakers' essays has meant a lopsided advancement in this field. In this paper, we report initial results of our experiments with nonnative AES that learns from manual evaluation of nonnative essays. For this purpose, we conducted an exercise in which essays written by nonnative English speakers in test environment were rated both manually and by the automated system designed for the experiment. In the process, we experimented with a few features to learn about nuances linked to nonnative evaluation. The proposed methodology of automated essay evaluation has yielded a correlation coefficient of 0.750 with the manual evaluation.
Kum Sung Middle School Number 2（金城第二中学）；KIM IL SUNG University（金日成大学）
Automated essay scoring plays an important role in judging students' language abilities in education. Traditional approaches use handcrafted features to score and are time-consuming and complicated. Recently, neural network approaches have improved performance without any feature engineering. Unlike other natural language processing tasks, only a small number of datasets are publicly available for automated essay scoring, and the size of the dataset is not sufficiently large. Considering that the performance of a neural network is closely related to the size of the dataset, the lack of data li…
While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automaticall…
Tohoku University（东北大学）；Riken Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Stony Brook University（石溪大学）
Existing approaches for automated essay scoring and document representation learning typically rely on discourse parsers to incorporate discourse structure into text representation. However, the performance of parsers is not always adequate, especially when they are used on noisy texts, such as student essays. In this paper, we propose an unsupervised pre-training approach to capture discourse structure of essays in terms of coherence and cohesion that does not require any discourse parser or annotation. We introduce several types of token, sentence and paragraph-level corruption techniques fo…
In this work, we present an approach based on combining string kernels and word embeddings for automatic essay scoring. String kernels capture the similarity among strings based on counting common character n-grams, which are a low-level yet powerful type of feature, demonstrating state-of-the-art results in various text classification tasks such as Arabic dialect identification or native language identification. To our best knowledge, we are the first to apply string kernels to automatically score essays. We are also the first to combine them with a high-level semantic feature representation,…
Fopure (Hangzhou) Technology Co., Ltd.（福珀（杭州）科技有限公司）；University of Science and Technology of China（中国科学技术大学）
Current state-of-art feature-engineered and end-to-end Automated Essay Score (AES) methods are proven to be unable to detect adversarial samples, e.g. the essays composed of permuted sentences and the prompt-irrelevant essays. Focusing on the problem, we develop a Two-Stage Learning Framework (TSLF) which integrates the advantages of both feature-engineered and end-to-end AES models. In experiments, we compare TSLF against a number of strong baselines, and the results demonstrate the effectiveness and robustness of our models. TSLF surpasses all the baselines on five-eighths of prompts and ach…
Unleashing Large Language Models' Proficiency in Zero-shot Essay Scoring
National Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）；School of Computer Science, Peking University（北京大学计算机学院）；School of Software and Microelectronics, Peking University（北京大学软件与微电子学院）
Guangdong University of Foreign Studies（广东外语外贸大学）；University of Montreal（蒙特利尔大学）
In the prompt-specific holistic score prediction task for Automatic Essay Scoring, the general approaches include pre-trained neural model, coherence model, and hybrid model that incorporate syntactic features with neural model. In this paper, we propose a novel approach to extract and represent essay coherence features with prompt-learning NSP that shows to match the state-of-the-art AES coherence model, and achieves the best performance for long essays. We apply syntactic feature dense embedding to augment BERT-based model and achieve the best performance for hybrid methodology for AES. In a…
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 te…
Most research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been some work done on scoring individual essay traits. In this paper, we describe a way to score essays holistically using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, and scoring the essay traits is the auxiliary task. We compare our results with a single-task learning (STL) approach, using both LSTMs and BiLSTMs. We also compare our results of the auxiliary task with such tasks done in other AEG systems. To find o…
Manipal University Jaipur（马尼帕尔大学斋浦尔分校）；BITS Pilani, K K Birla Goa Campus（比拉理工学院皮拉尼分校果阿校区）；Indraprastha Institute of Information Technology, Delhi（德里英迪拉普拉沙信息技术学院）；Pune Institute of Computer Technology（浦那计算机技术学院）；Sardar Vallabhbhai National Institute of Technology, Surat（萨达尔·瓦拉巴伊国家技术学院苏拉特分校）；University of Allahabad（阿拉哈巴德大学）；Banaras Hindu University（贝拿勒斯印度教大学）
The use of Natural Language Processing (NLP) for Automated Essay Scoring (AES) has been well explored in the English language, with benchmark models exhibiting performance comparable to human scorers. However, AES in Hindi and other low-resource languages remains unexplored. In this study, we reproduce and compare state-of-the-art methods for AES in the Hindi domain. We employ classical feature-based Machine Learning (ML) and advanced end-to-end models, including LSTM Networks and Fine-Tuned Transformer Architecture, in our approach and derive results comparable to those in the English languag…
Automated Essay Scoring and Language Certification: Assessing Generalizability, Agreement and Validity for French
University of Exeter（埃克塞特大学）；France Éducation international（法国教育国际）；Cental, IL&C, UCLouvain；Computer Science and Engineering Department, Universidad Carlos III de Madrid（马德里卡斯蒂利亚大学计算机科学与工程系）
In this article, an automated system is proposed for essay scoring in Arabic language for online exams based on stemming techniques and Levenshtein edit operations. An online exam has been developed on the proposed mechanisms, exploiting the capabilities of light and heavy stemming. The implemented online grading system has shown to be an efficient tool for automated scoring of essay questions.
Automated essay scoring is one of the most important problem in Natural Language Processing. It has been explored for a number of years, and it remains partially solved. In addition to its economic and educational usefulness, it presents research problems. Transfer learning has proved to be beneficial in NLP. Data augmentation techniques have also helped build state-of-the-art models for automated essay scoring. Many works in the past have attempted to solve this problem by using RNNs, LSTMs, etc. This work examines the transformer models like BERT, RoBERTa, etc. We empirically demonstrate the…
PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
Department of Educational Psychology, East China Normal University（华东师范大学教育心理学系）；Shanghai Institute of Artificial Intelligence for Education, East China Normal University（华东师范大学上海智能教育研究院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
LAIX Inc.（流利说公司）；Shanghai Key Laboratory of Artificial Intelligence in Learning and Cognitive Science（上海学习与认知科学人工智能重点实验室）
In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks. However, in the area of Automated Essay Scoring (AES), pre-trained models such as BERT have not been properly used to outperform other deep learning models such as LSTM. In this paper, we introduce a novel multi-scale essay representation for BERT that can be jointly learned. We also employ multiple losses and transfer learning from out-of-domain essays to further improve the performance. Experiment results show that our approach derives much benefit from joint learning of multi-scale essa…
Composable Cross-prompt Essay Scoring by Merging Models
National Key Laboratory for Multimedia Information Processing（国家多媒体信息处理重点实验室）；Peking University School of Computer Science（北京大学计算机学院）；Peking University（北京大学）
Automated essay scoring (AES) is gaining increasing attention in the education sector as it significantly reduces the burden of manual scoring and allows ad hoc feedback for learners. Natural language processing based on machine learning has been shown to be particularly suitable for text classification and AES. While many machine-learning approaches for AES still rely on a bag-of-words (BOW) approach, we consider a transformer-based approach in this paper, compare its performance to a logistic regression model based on the BOW approach and discuss their differences. The analysis is based on 2…
In this paper, we have introduced and evaluated intonation based feature for scoring the English speech of nonnative English speakers in Indian context. For this, we created an automated spoken English scoring engine to learn from the manual evaluation of spoken English. This involved using an existing Automatic Speech Recognition (ASR) engine to convert the speech to text. Thereafter, macro features like accuracy, fluency and prosodic features were used to build a scoring model. In the process, we introduced SimIntonation, short for similarity between spoken intonation pattern and "ideal" i.e…
