Since Ellis Page (1966) published his landmark article, “The Imminence of Grading Essays by Computer,” the issue of using computers to grade writing and provide feedback has been a great concern for researchers in language testing and writing instruction. Research has been done for the creation and development of systems for automated essay evaluation (AEE), or automated essay scoring (AES), wh...

Neural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input essays, giving grades based on a single vector representation of the essay. On the other hand, the relative advantages of RNNs and CNNs have not been compared. In addition, different parts...

Essay scoring is a complicated processing requiring analyzing, summarizing and judging expertise. Traditional work on essay scoring focused on automatic handcrafted features, which are expensive yet sparse. Neural models offer a way to learn syntactic and semantic features automatically, which can potentially improve upon discrete features. In this paper, we employ convolutional neural network ...

Conventional Automated Essay Scoring (AES) measures may cause severe problems when directly applied in scoring Automatic Speech Recognition (ASR) transcription as they are error sensitive and unsuitable for the characteristic of ASR transcription. Therefore, we introduce a framework of Finite State Transducer (FST) to avoid the shortcomings. Compared with the Latent Semantic Analysis with Suppo...

Existing software systems for automated essay scoring can provide NLP researchers with opportunities to test certain theoretical hypotheses, including some derived from Centering Theory. In this study we employ the Educational Testing Service’s e-rater essay scoring system to examine whether local discourse coherence, as defined by a measure of Centering Theory’s Rough-Shift transitions, might ...

This study exploits statistical redundancy inherent in natural language to automatically predict scores for essays. We use a hybrid feature identification method, including syntactic structure analysis, rhetorical structure analysis, and topical analysis, to score essay responses from test-takers of the Graduate Management Admissions Test (GMAT) and the Test of Written English (TWE). For each e...

This paper addresses the ongoing discussion on influencing factors of automatic essay scoring with latent semantic analysis (LSA). Throughout this paper, we contribute to this discussion by presenting evidence for the effects of the parameters text pre-processing, weighting, singular value dimensionality and type of similarity measure on the scoring results. We benchmark this effectiveness by c...