A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data.
In this paper, we introduce the Recovering Difference Softmax Algorithm to address the particular challenges of this problem domain, and use it to successfully optimize millions of daily reminders for the online language-learning app Duolingo.
We present the task of Simultaneous Translation and Paraphrasing for Language Education (STAPLE).
We describe a method for rapidly creating language proficiency assessments, and provide experimental evidence that such tests can be valid, reliable, and secure.
This paper describes DUALIST, an active learning annotation paradigm which solicits and learns from labels on both features (e. g., words) and instances (e. g., documents).
As the wealth of biomedical knowledge in the form of literature increases, there is a rising need for effective natural language processing tools to assist in organizing, curating, and retrieving this information.