REVIEW 1 cited by
A Survey of Active Learning for Natural Language Processing
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
A Survey of Active Learning for Natural Language Processing
read the original abstract
In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of applying AL to NLP problems. These include AL for structured prediction tasks, annotation cost, model learning (especially with deep neural models), and starting and stopping AL. Finally, we conclude with a discussion of related topics and future directions.
Forward citations
Cited by 1 Pith paper
-
CodeGENCAT: Generative Computerized Adaptive Testing for Open-ended Coding Problems
A generative model of student code responses improves early-stage adaptive-test question selection, yielding up to 4.32% higher AUC than a 1PL baseline.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.