Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models

Sigrid Klerke; Héctor  Martínez Alonso; Barbara Plank

doi:10.18653/v1/W18-0523

Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models

Sigrid Klerke
, Héctor Martínez Alonso
, Barbara Plank

Thomson Reuters

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

We present our submission to the 2018 Duolingo Shared Task on Second Language
Acquisition Modeling (SLAM). We focus on evaluating a range of features for the task, including user-derived measures, while examining how far we can get with a simple linear classifier. Our analysis reveals that errors differ per exercise format, which motivates our final and best-performing system: a task-wise (per exercise-format) model.

Original language	English
Title of host publication	Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications : NAACL HLT 2018
Place of Publication	New Orleans
Publisher	Association for Computational Linguistics
Publication date	2018
Pages	206-211
ISBN (Print)	978-1-948087-11-7
DOIs	https://doi.org/10.18653/v1/W18-0523
Publication status	Published - 2018

Keywords

Second Language Acquisition
SLAM
Linear Classifier
Exercise Format
User-Derived Measures

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10.18653/v1/W18-0523

sharedtask-duolingo-2018Accepted author manuscript, 675 KB

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Klerke, S., Martínez Alonso, H., & Plank, B. (2018). Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications: NAACL HLT 2018 (pp. 206-211). Association for Computational Linguistics. https://doi.org/10.18653/v1/W18-0523

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abstract = "We present our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We focus on evaluating a range of features for the task, including user-derived measures, while examining how far we can get with a simple linear classifier. Our analysis reveals that errors differ per exercise format, which motivates our final and best-performing system: a task-wise (per exercise-format) model.",

keywords = "Second Language Acquisition, SLAM, Linear Classifier, Exercise Format, User-Derived Measures, Second Language Acquisition, SLAM, Linear Classifier, Exercise Format, User-Derived Measures",

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Klerke, S, Martínez Alonso, H & Plank, B 2018, Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models. in Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications: NAACL HLT 2018. Association for Computational Linguistics, New Orleans, pp. 206-211. https://doi.org/10.18653/v1/W18-0523

Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models. / Klerke, Sigrid; Martínez Alonso, Héctor ; Plank, Barbara.
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications: NAACL HLT 2018. New Orleans: Association for Computational Linguistics, 2018. p. 206-211.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

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KW - Linear Classifier

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KW - User-Derived Measures

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Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models

Abstract

Keywords

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