7 citations · 7 across the 2 of their papers we have counts for
6 papers
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification
Allen Schmaltz, Andrew Beam
We present a novel end-to-end language model for joint retrieval and classification, unifying the strengths of bi- and cross- encoders into a single language model via a coarse-to-…
Exemplar Auditing for Multi-Label Biomedical Text Classification
Allen Schmaltz, Andrew Beam
Many practical applications of AI in medicine consist of semi-supervised discovery: The investigator aims to identify features of interest at a resolution more fine-grained than th…
Ecological Regression with Partial Identification
Wenxin Jiang, Gary King, Allen Schmaltz +1
Ecological inference (EI) is the process of learning about individual behavior from aggregate data. We study a partially identified linear contextual effects model for EI and descr…
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Andrew L. Beam, Benjamin Kompa, Allen Schmaltz +6
Word embeddings are a popular approach to unsupervised learning of word relationships that are widely used in natural language processing. In this article, we present a new set of…
Adapting Sequence Models for Sentence Correction
Allen Schmaltz, Yoon Kim, Alexander M. Rush +1
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based…
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
Allen Schmaltz, Yoon Kim, Alexander M. Rush +1
We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AES…