Interpretable contrastive word mover's embedding

1 code implementation • 1 Nov 2021 • Ruijie Jiang, Julia Gouvea, Eric Miller, David Hammer, Shuchin Aeron

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.

Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space

no code implementations • 26 Nov 2020 • Ruijie Jiang, Julia Gouvea, David Hammer, Eric Miller, Shuchin Aeron

This work is a step towards building a statistical machine learning (ML) method for achieving an automated support for qualitative analyses of students' writing, here specifically in score laboratory reports in introductory biology for sophistication of argumentation and reasoning.

BIG-bench Machine Learning Contrastive Learning +4