Interpretable contrastive word mover's embedding
Ruijie Jiang, Julia Gouvea, Eric Miller, David Hammer, Shuchin Aeron

TL;DR
This paper enhances contrastive Word Mover's Embedding by adding interpretability through clustering, improving classification performance and providing keyword-based explanations, with applications in educational assessment of student scientific writing.
Contribution
It introduces a clustering-promoting mechanism into contrastive embedding, enabling interpretability and better classification in NLP tasks, especially for educational assessment.
Findings
Significant performance improvement over baselines.
Effective keyword-based interpretation of clusters.
Useful in assessing student scientific writing.
Abstract
This paper shows that a popular approach to the supervised embedding of documents for classification, namely, contrastive Word Mover's Embedding, can be significantly enhanced by adding interpretability. This interpretability is achieved by incorporating a clustering promoting mechanism into the contrastive loss. On several public datasets, we show that our method improves significantly upon existing baselines while providing interpretation to the clusters via identifying a set of keywords that are the most representative of a particular class. Our approach was motivated in part by the need to develop Natural Language Processing (NLP) methods for the \textit{novel problem of assessing student work for scientific writing and thinking} - a problem that is central to the area of (educational) Learning Sciences (LS). In this context, we show that our approach leads to a meaningful…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Text Analysis Techniques
