44 citations · 64 across the 4 of their papers we have counts for
8 papers
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration
James Sharpnack, Kevin Hao, Phoebe Mulcaire +4
In this paper, we present a complete framework for quickly calibrating and administering a robust large-scale computerized adaptive test (CAT) with a small number of responses. Cal…
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell +2
Item response theory (IRT) is a class of interpretable factor models that are widely used in computerized adaptive tests (CATs), such as language proficiency tests. Traditionally,…
Grounded Compositional Outputs for Adaptive Language Modeling
Nikolaos Pappas, Phoebe Mulcaire, Noah A. Smith
Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Matt Gardner, Yoav Artzi, Victoria Basmova +23
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluation…
Low-Resource Parsing with Crosslingual Contextualized Representations
Phoebe Mulcaire, Jungo Kasai, Noah A. Smith
Despite advances in dependency parsing, languages with small treebanks still present challenges. We assess recent approaches to multilingual contextual word representations (CWRs),…
Polyglot Contextual Representations Improve Crosslingual Transfer
Phoebe Mulcaire, Jungo Kasai, Noah A. Smith
We introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages. Our method combines the a…