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Learning @ Scale is a relatively new venue for NLP research in education.
MOOCs now incorporate AWE systems to manage several thousand assignments that may be received during a single MOOC course.
We expect that the workshop will continue to highlight novel technologies and opportunities for educational NLP in English as well as other languages.
The workshop will solicit both full papers and short papers for either oral or poster presentation.
Another breakthrough for educational applications within the CL community is the presence of a number of shared-task competitions over the past several years — including three shared tasks on grammatical error detection and correction.
NLP/Education shared tasks have seen new areas of research, such as the Automated Evaluation of Scientific Writing at BEA 11, Native Language Identification at BEA 12, and Second Language Acquisition Modelling and Complex Word Identification both at BEA 13.
This means systems have increasingly overfit to a very specific type of English and so do not generalise well to other domains.
Our proposal hence introduces a new dataset that represents a much more diverse cross-section of English language domains. Rubric Reliability and Annotation of Content and Argument in Source-Based Argument Essays. We will be using the ACL Submission Guidelines for the BEA Workshop this year.
In the writing and speech domains, automated writing evaluation (AWE) and speech scoring applications, respectively, are commercially deployed in high-stakes assessment and in instructional contexts (e.g., Massive Open Online Courses (MOOCs) and K-12 classrooms).
First, the Hewlett Foundation reached out to the public and private sectors and sponsored two competitions: one for automated essay scoring, and the other for scoring of short response items.
The motivation driving these competitions was to engage the larger scientific community in this enterprise.
GEC gained significant attention in the HOO and Co NLL shared tasks between 20 (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., 2013; Ng et al., 2014), but has since become much more difficult to evaluate given a lack of standardised experimental settings.
In particular, recent systems have been trained, tuned and tested on different combinations of corpora using different metrics (Yannakoudakis et al., 2017; Chollampatt and Ng, 2018a; Ge et al., 2018; Junczys-Dowmunt et al., 2018).
These competitions increased the visibility of, and interest in, our field.