Interpretable contrastive word mover's embedding

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 assessment of the student work related to lab reports from a biology class and can help LS researchers gain insights into student understanding and assess evidence of scientific thought processes.