Automated essay scoring with string kernels and word embeddings (1804.07954v2)

Abstract: 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, namely the bag-of-super-word-embeddings. We report the best performance on the Automated Student Assessment Prize data set, in both in-domain and cross-domain settings, surpassing recent state-of-the-art deep learning approaches.

• The paper introduces a hybrid approach combining string kernels and bag-of-super-word embeddings to improve automated essay scoring accuracy.
• The methodology leverages both character n-grams and semantic clustering, achieving a 0.785 performance score in in-domain experiments.
• The fusion of low-level syntactic and high-level semantic features offers significant gains in cross-domain scenarios compared to deep learning models.

Overview of "Automated Essay Scoring with String Kernels and Word Embeddings"

The paper "Automated Essay Scoring with String Kernels and Word Embeddings," authored by Cozma, Butnaru, and Ionescu, introduces a novel method for automated essay scoring (AES). This methodology integrates the use of string kernels alongside word embeddings to enhance essay evaluation in educational settings. By capitalizing on both low-level character n-grams and high-level semantic representations, this approach aspires to yield results superior to recent deep learning configurations, evidenced by its performance on the Automated Student Assessment Prize data set.

Key Methodological Components

1. String Kernels: These apply character n-grams to compute similarity measurements among text samples. Their application in text classification tasks has already demonstrated efficacy. Within this paper, the authors leverage string kernels to evaluate the AES task effectively.
2. Bag-of-super-word-embeddings (BOSWE): This technique derives from the word2vec framework, utilizing k-means clustering to form super-word vectors that encapsulate semantic information. The BOSWE method transforms essays into structured representations that can be processed using kernel-based regression models.
3. Model Fusion: The authors advocate summing kernel matrices derived from both string kernels and BOSWE to improve predictive performance, leveraging the rich feature space inherent in both approaches.

Experimental Results and Analysis

The authors rigorously evaluate their approach within two experimental settings: in-domain and cross-domain. Results indicate:

• In in-domain experiments, their combined approach surpasses existing baselines, achieving an overall score (0.785) higher than other state-of-the-art methods cited in the paper. The combination of string kernels with BOSWE results in enhanced performance across nearly all tested prompts.
• In cross-domain scenarios, notable improvements are observed compared to contemporary baseline methods, with enhancements of over 10% in various cases. Notably, the contribution of string kernels alone in cross-domain applications sometimes outperforms combined models, particularly with limited in-domain sample availability.

Theoretical Contributions and Potential Implications

This exploration of leveraging both low-level syntactic and high-level semantic attributes underscores a crucial advancement in algorithmic compositions for AES tasks. The integration of string kernels and BOSWE as dual-form kernels bridges feature-rich text representations while maintaining computational simplicity. The effectiveness of these methods prompts reconsideration of the increasingly common reliance on deep learning models alone, suggesting that hybrid approaches can often yield superior results.

Future Directions

Several avenues for future research stem from this paper. Venturing into more complex LLMs or extending the application of these kernel methods to broader multilingual contexts may uncover further advancements. Additionally, devising explicit embedding maps could allow for enhanced interpretability in AES tasks, offering profound insights into what specific textual features most influence automated scores.

In essence, the combination of string kernels and word embeddings presents a potent alternative to traditional AES methods, inviting a revised perspective on the marriage of shallow and deep learning paradigms in enhancing natural language processing capabilities.