3260 papers • 126 benchmarks • 313 datasets
Essay scoring: Automated Essay Scoring is the task of assigning a score to an essay, usually in the context of assessing the language ability of a language learner. The quality of an essay is affected by the following four primary dimensions: topic relevance, organization and coherence, word usage and sentence complexity, and grammar and mechanics. Source: A Joint Model for Multimodal Document Quality Assessment
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These leaderboards are used to track progress in automated-essay-scoring
Use these libraries to find automated-essay-scoring models and implementations
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After adding some adversarial essays to the original datasets, TSLF outperforms the feature-engineered and end-to-end baselines to a great extent, and shows great robustness.
A neural model of local coherence is developed that can effectively learn connectedness features between sentences, and a framework for integrating and jointly training theLocal coherence model with a state-of-the-art AES model is proposed.
A new neural architecture that enhances vanilla neural network models with auxiliary neural coherence features with state-of-the-art performance on the benchmark ASAP dataset, outperforming not only feature engineering baselines but also other deep learning models.
It is shown that the co-attention based neural network model provides reliable score prediction of source-dependent responses and the attention of the model is similar to the expert opinions with examples.
A model agnostic adversarial evaluation scheme and associated metrics for AES systems to test their natural language understanding capabilities and overall robustness and it is found that AES models are highly overstable such that even heavy modifications with content unrelated to the topic of the questions do not decrease the score produced by the models.
PAES is easy to apply in practice and achieves state-of-the-art performance on the Automated Student Assessment Prize (ASAP) dataset and requires no access to labelled or unlabelled target-prompt data during training and is a single-stage approach.
A way to score essays using a multi-task learning (MTL) approach, where scoring the essay holistically is the primary task, andscoring the essay traits is the auxiliary task, is described.
expats, an open-source framework to allow its users to develop and experiment with different ATS models quickly by offering flexible components, an easy-to-use configuration system, and the command-line interface, and provides seamless integration with the Language Interpretability Tool (LIT).
The current state-of-the-art natural language processing (NLP) neural network architectures are used in this work to achieve above human-level accuracy on the publicly available Kaggle AES dataset.
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