PRELIMINARY CALL FOR PAPERS
The 8th Workshop on the Innovative Use of NLP for Building
Educational Applications (BEA8)
Atlanta, Georgia, USA; June 13 or 14, 2013
(co-located with NAACL-HLT)
Submission Deadline: March 11, 2013 (tentative)
Research in NLP applications for education continues to progress using innovative NLP techniques - statistical, rule-based, or most commonly, a combination of the two. New technologies have made it possible to include speech in both assessment and Intelligent Tutoring Systems (ITS). NLP techniques are also being used to generate assessments, and tools for curriculum development of reading materials, as well as tools to support assessment and test development. As a community, we continue to improve existing capabilities and to identify and generate innovative and creative ways to use NLP in applications for writing, reading, speaking, critical thinking, and assessment.
In 2012, the use of NLP in educational contexts took two major steps forward. First, outside of the computational linguistics community, the Hewlett Foundation reached out to both the public and private sectors and sponsored two competitions: one on automated essay scoring (Automated Student Assessment Prize: ASAP, Phase 1:http://www.kaggle.com/c/asap-aes), and a second on short-answer scoring (Phase 2: http://www.kaggle.com/c/asap-sas). The motivation driving these competitions was to engage the larger scientific community in this enterprise. The two competitions were inspired by the Common Core State Initiative (http://www.corestandards.org/), an influential set of standards adopted by 45 states in the U.S. The Initiative describes what K-12 students should be learning with regard to Reading, Writing, Speaking, Listening, and Media and Technology. Another breakthrough for educational applications within the computational linguistics community was the second edition of the "Helping Our Own" grammatical error detection/correction competition at last year's BEA workshop - where 14 systems competed. In 2013, independent of the BEA workshop, there will be two shared task competitions. This year's CoNLL Shared Task is on grammatical error correction and there is a SemEval Shared Task on Student Response Analysis (http://www.cs.york.ac.uk/semeval-2013/task7/). Both of these competitions will increase the visibility of the educational problem space in the NLP community.
In this year's BEA workshop, we are soliciting papers across a broad range of educational applications, including: intelligent tutoring, learner cognition, use of corpora, grammatical error detection, tools for teachers and test developers, and automated scoring and evaluation of open-ended responses. Since the first workshop in 1997, "Innovative Use of NLP in Building Educational Applications" has continued to bring together all NLP subfields to foster interaction and collaboration among researchers in both academic institutions and industry. The workshop offers a venue for researchers to present and discuss their work in these areas. Each year, we see steady growth in workshop submissions and attendance, and the research has become more innovative and advanced. In 2013, we expect that the workshop (consistent with previous workshops at ACL 1997, NAACL/HLT 2003, ACL 2005, ACL 2008, NAACL HLT 2009, NAACL HLT 2010, ACL 2011, NAACL HLT 2012), will continue to expose the NLP research community to technologies that identify novel opportunities for the use of NLP techniques and tools in educational applications. At NAACL HLT 2012, the workshop coordinated with the HOO shared task for grammatical error detection, generating a much larger poster session that was lively and well-attended. In 2013, the workshop will host the first Native Language Identification Shared Task, described in detail later in this cfp.
The workshop will solicit both full papers and short papers for either oral or poster presentation. Given the broad scope of the workshop, we organize the workshop around three central themes in the educational infrastructure:
1. Development of curriculum and assessment (e.g., applications that help teachers develop reading materials) 2. Delivery of curriculum and assessments (e.g., applications where the student receives instruction and interacts with the system); 3. Reporting of assessment outcomes (e.g., automated scoring of open-ended responses)
Topics will include, but will not be limited to, the following:
Automated scoring/evaluation for oral and written student responses * Content analysis for scoring/assessment * Analysis of the structure of argumentation * Grammatical error detection and correction * Discourse and stylistic analysis * Plagiarism detection * Machine translation for assessment, instruction and curriculum development * Detection of non-literal language (e.g., metaphor) * Sentiment analysis
Intelligent Tutoring (IT) that incorporates state-of-the-art NLP methods * Dialogue systems in education * Hypothesis formation and testing * Multi-modal communication between students and computers * Generation of tutorial responses * Knowledge representation in learning systems * Concept visualization in learning systems
Learner cognition * Assessment of learners' language and cognitive skill levels * Systems that detect and adapt to learners' cognitive or emotional states * Tools for learners with special needs
Use of corpora in educational tools * Data mining of learner and other corpora for tool building * Annotation standards and schemas / annotator agreement
Tools and applications for classroom teachers and/or test developers * NLP tools for second and foreign language learners * Semantic-based access to instructional materials to identify appropriate texts * Tools that automatically generate test questions * Processing of and access to lecture materials across topics and genres * Adaptation of instructional text to individual learners' grade levels * Tools for text-based curriculum development * E-learning tools for personalized course content * Language-based educational games
Issues concerning the evaluation of NLP-based educational tools
Descriptions of implemented systems
Descriptions and proposals for shared tasks
We will be using the NAACL-HLT 2013 Submission Guidelines for the BEA-8 Workshop this year. Authors are invited to submit a full paper of up to 8 pages in electronic, PDF format, with up to 2 additional pages for references. We also invite short papers of up to 4 pages, including 2 additional pages for references. Papers which describe systems are also invited to give a demo of their system.
Previously published papers cannot be accepted. The submissions will be reviewed by the program committee. As reviewing will be blind, please ensure that papers are anonymous. Self-references that reveal the author's identity, e.g., "We previously showed (Smith, 1991) ...", should be avoided. Instead, use citations such as "Smith previously showed (Smith, 1991) ...".
Please use the 2013 NAACL-HLT style sheet for composing your paper: http://naacl2013.naacl.org/CFP.aspx (see Format section for style files).
Submission Deadline: March 11 (tentative) Notification of Acceptance: March 29 Camera-ready papers Due: May 04 Workshop: June 13 or 14
Joel Tetreault, ETS, USA (principal contact: bea8.workshop at gmail.com) Jill Burstein, ETS, USA Claudia Leacock, CTB McGraw-Hill, USA
NATIVE LANGUAGE IDENTIFICATION SHARED TASK
We are pleased to host the first edition of a shared task on Native Language Identification (NLI). The shared task will be organized by Joel Tetreault, Aoife Cahill (ETS) and Daniel Blanchard (ETS). NLI is the task of identifying the native language (L1) of a writer based solely on a sample of their writing. The task is typically framed as a classification problem where the set of L1s is known a priori. Most work has focused on identifying the native language of writers learning English as a second language. To date this topic has motivated several ACL and EMNLP papers, as well as a master's thesis.
Native Language Identification (NLI) can be useful for a number of applications. In educational settings, NLI can be used to provide more targeted feedback to language learners about their errors. It is well known that learners of different languages make different errors depending on their L1s. A writing tutor system which can detect the native language of the learner will be able to tailor the feedback about the error and contrast it with common properties of the learner's language. In addition, native language is often used as a feature that goes into authorship profiling, which is frequently used in forensic linguistics.
Details on the shared task can be found on the website: https://sites.google.com/site/nlisharedtask2013/home
Andrea Abel, EURAC, Italy Sumit Basu, Microsoft Research, USA Lee Becker, Avaya Labs, USA Beata Beigman Klebanov, Educational Testing Service, USA Delphine Bernhard, Université de Strasbourg, France Jared Bernstein, Pearson, USA Kristy Boyer, North Carolina State University, USA Chris Brew, Educational Testing Service, USA Ted Briscoe, University of Cambridge, UK Chris Brockett, MSR, USA Aoife Cahill, Educational Testing Service, USA Martin Chodorow, Hunter College, CUNY, USA Mark Core, USC Institute for Creative Technologies, USA Daniel Dahlmeier, National University of Singapore, Singapore Markus Dickinson, Indiana University, USA Bill Dolan, Microsoft, USA Myrosia Dzikovska, University of Edinburgh, UK Keelan Evanini, Educational Testing Service, USA Michael Flor, Educational Testing Service, USA Peter Foltz, Pearson Knowledge Technologies, USA Jennifer Foster, Dublin City University, Ireland Horacio Franco, SRI, USA Michael Gamon, Microsoft, USA Caroline Gasperin, SwiftKey, UK Kallirroi Georgila, USC Institute for Creative Technologies, USA Iryna Gurevych, University of Darmstadt, Germany Kadri Hacioglu, Rossetta Stone, USA Na-Rae Han, University of Pittsburgh, USA Trude Heift, Simon Frasier University, Canada Michael Heilman, Educational Testing Service, USA Derrick Higgins, Educational Testing Service, USA Ross Israel, Indiana University, USA Heng Ji, Queens College, USA Pamela Jordan, University of Pittsburgh, USA Ola Knutsson, KTH Nada, Sweden John Lee, City University of Hong Kong, China Jackson Liscombe, Nuance Communications, USA Diane Litman, University of Pittsburgh, USA Annie Louis, University of Pennsylvania, USA Xiaofei Lu, Penn State University, USA Nitin Madnani, Educational Testing Service, USA Montse Maritxalar, University of the Basque Country, Spain James Martin, University of Colorado, USA Aurélien Max, LIMSI-CNRS, France Detmar Meurers, University of Tübingen, Germany Lisa Michaud, Merrimack College, USA Michael Mohler, University of North Texas Smaranda Muresan, Rutgers University, USA Ani Nenkova, University of Pennsylvania, USA Hwee Tou Ng, National University of Singapore, Singapore Rodney Nielsen, University of North Texas, USA Ted Pedersen, University of Minnesota, USA Bryan Pellom, Rossetta Stone, USA Patti Price, PPRICE Speech and Language Technology, USA Andrew Rosenberg, Queens College, CUNY, USA Mihai Rotaru, TextKernel, The Netherlands Dan Roth, UIUC, USA Alla Rozovskaya, UIUC, USA Izhak Shafran, Oregon Health & Science University, USA Serge Sharoff, University of Leeds, UK Richard Sproat, Google, USA Svetlana Stenchikova, Columbia University, USA Helmer Strik, Radboud University Nijmegen, The Netherlands Joseph Tepperman, Rosetta Stone, USA Nai-Lung Tsao, National Central University, Taiwan Monica Ward, Dublin City University, Ireland Pete Whitelock, Oxford University Press, UK David Wible, National Central University, Taiwan Peter Wood, University of Saskatchewan in Saskatoon, Canada Klaus Zechner, Educational Testing Service, USA Torsten Zesch, University of Darmstadt, Germany