Automatic Text Scoring Using Neural Networks (1606.04289v2)

Abstract: Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We introduce a model that forms word representations by learning the extent to which specific words contribute to the text's score. Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches. In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model has found more discriminative.

• The paper introduces a model that leverages LSTMs and score-specific word embeddings to eliminate manual feature engineering in text scoring.
• It achieves superior performance with a Pearson correlation of 0.96 and RMSE of 2.4 on the Kaggle dataset compared to traditional methods.
• The study offers visualization techniques that enhance interpretability by identifying key words influencing the predicted scores.

Automated Text Scoring Using Neural Networks: An Expert Analysis

The paper "Automatic Text Scoring Using Neural Networks" by Dimitrios Alikaniotis, Helen Yannakoudakis, and Marek Rei explores the utilization of deep learning methodologies for Automated Text Scoring (ATS), with a particular focus on using recurrent neural networks (RNNs), specifically Long Short-Term Memory networks (LSTMs), for this purpose. It addresses the challenges of manual feature engineering in traditional ATS systems and proposes an approach that automatically generates score-specific word embeddings (SSWEs) that can improve text scoring performance.

Key Contributions of the Study

The authors propose several innovations that enhance the process of ATS:

1. Automatic Feature Learning: The paper leverages neural networks to automatically learn relevant features from text data, bypassing the need for manually engineered features. This is achieved through the development of score-specific word embeddings, which capture not only linguistic context but also evaluative criteria that relate to text quality.
2. Use of Recurrent Neural Networks: Employing LSTMs, the paper demonstrates the ability to generate effective embeddings for entire essays. The model considers text as a sequence of tokens, extracting significant feature representations that contribute directly to the scoring process. Both unidirectional and bidirectional LSTM architectures are explored.
3. Visualization and Interpretability: The authors recognize the 'black box' nature of deep learning models and present a visualization technique to highlight which words in the text contribute meaningfully to the predicted score. This tool assesses the 'quality' of word vectors by evaluating gradient magnitudes using pseudo-scores, thereby offering insights into the neural network's decision-making process.

Results and Implications

The results indicate that the SSWE combined with LSTMs achieves superior performance metrics compared to traditional approaches (e.g., Support Vector Machines) and other deep learning models. Notably, the best model configuration improves the Pearson's correlation coefficient (r = 0.96) and reduces the Root Mean Square Error (RMSE to 2.4), outperforming state-of-the-art results on the Kaggle dataset used in the paper. This suggests that the proposed method can evaluate writing quality with high accuracy, making it a viable alternative to human grading in suitable contexts.

Theoretically, the approach signals a significant progression in the field of natural language processing by showcasing how neural networks can autonomously learn and apply complex evaluation criteria. Practically, this method has applications in educational technology, where scalable, accurate ATS can support instructors through more efficient grading processes and provide detailed feedback to students.

Future Prospects

The implementation of SSWEs represents a back-to-basics approach to embedding learning by emphasizing domain-specific utility. Future work could involve experimenting with larger datasets, exploring multi-task learning formats, or extending this framework to support multilingual ATS. Moreover, improvements in interpretability and feedback mechanisms promise further integration of neural networks into semantic understanding tasks.

In conclusion, this paper presents a compelling case for employing neural networks for ATS and extends the current understanding of how deep learning can facilitate text evaluation tasks at a level comparable, if not superior, to human performance. Its implications reach across educational technology, automated evaluation systems, and broader applications in natural language processing.