تقرير
Interpretable contrastive word mover's embedding
| العنوان: | Interpretable contrastive word mover's embedding |
|---|---|
| المؤلفون: | Jiang, Ruijie, Gouvea, Julia, Miller, Eric, Hammer, David, Aeron, Shuchin |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | 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. Comment: 8 pages, 4 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2111.01023 |
| رقم الأكسشن: | edsarx.2111.01023 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |