Deep learning has demonstrated tremendous potential for Automatic Text Scoring (ATS) tasks. In this paper, we describe a new neural architecture that enhances vanilla neural network models with auxiliary neural coherence features. Our new method proposes a new mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads. Subsequently, the semantic relationships between multiple snapshots are used as auxiliary features for prediction. This has two main benefits. Firstly, essays are typically long sequences and therefore the memorization capability of the LSTM network may be insufficient. Implicit access to multiple snapshots can alleviate this problem by acting as a protection against vanishing gradients. The parameters of the mechanism also acts as an auxiliary memory. Secondly, modeling relationships between multiple positions allows our model to learn features that represent and approximate textual coherence. In our model, we call this features. Overall, we present a unified deep learning architecture that generates neural coherence features as it reads in an end-to-end fashion. Our approach demonstrates state-of-the-art performance on the benchmark ASAP dataset, outperforming not only feature engineering baselines but also other deep learning models.
The reliance on handcrafted features is a central theme to decades of ATS research. The complexity and ease of implementation of essay scoring features can be diverse. For example, length-based features are intuitive and simple to extract from essays. On the other hand, there are more complex features such as grammar correctness or lexical complexity. Features such as readability @cite_14 , textual and discourse coherence @cite_12 @cite_6 are also harder to design in which convoluted pipelines have to be built for feature extraction to be performed. As a whole, feature engineering is generally a laborious process, i.e., apart from designing features, custom code has to be written for each additional feature. For a comprehensive review of feature engineering in the context of ATS, we refer interested readers to @cite_14 .
@cite_14 @cite_6 @cite_12
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Automated scoring of student essays is increasingly used to reduce manual grading effort. State-of-the-art approaches use supervised machine learning which makes it complicated to transfer a system trained on one task to another. We investigate which currently used features are task-independent and evaluate their transferability on English and German datasets. We find that, by using our task-independent feature set, models transfer better between tasks. We also find that the transfer works even better between tasks of the same type. This paper presents an investigation of lexical chaining (Morris and Hirst, 1991) for measuring discourse coherence quality in test-taker essays. We hypothesize that attributes of lexical chains, as well as interactions between lexical chains and explicit discourse elements, can be harnessed for representing coherence. Our experiments reveal that performance achieved by our new lexical chain features is better than that of previous discourse features used for this task, and that the best system performance is achieved when combining lexical chaining features with complementary discourse features, such as those provided by a discourse parser based on rhetorical structure theory, and features that reflect errors in grammar, word usage, and mechanics. Previous approaches for automated essay scoring (AES) learn a rating model by minimizing either the classification, regression, or pairwise classification loss, depending on the learning algorithm used. In this paper, we argue that the current AES systems can be further improved by taking into account the agreement between human and machine raters. To this end, we propose a rankbased approach that utilizes listwise learning to rank algorithms for learning a rating model, where the agreement between the human and machine raters is directly incorporated into the loss function. Various linguistic and statistical features are utilized to facilitate the learning algorithms. Experiments on the publicly available English essay dataset, Automated Student Assessment Prize (ASAP), show that our proposed approach outperforms the state-of-the-art algorithms, and achieves performance comparable to professional human raters, which suggests the effectiveness of our proposed method for automated essay scoring.