Adam Wiemerslage


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Multiple Instance Learning for Content Feedback Localization without Annotation
Scott Hellman | William Murray | Adam Wiemerslage | Mark Rosenstein | Peter Foltz | Lee Becker | Marcia Derr
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

Automated Essay Scoring (AES) can be used to automatically generate holistic scores with reliability comparable to human scoring. In addition, AES systems can provide formative feedback to learners, typically at the essay level. In contrast, we are interested in providing feedback specialized to the content of the essay, and specifically for the content areas required by the rubric. A key objective is that the feedback should be localized alongside the relevant essay text. An important step in this process is determining where in the essay the rubric designated points and topics are discussed. A natural approach to this task is to train a classifier using manually annotated data; however, collecting such data is extremely resource intensive. Instead, we propose a method to predict these annotation spans without requiring any labeled annotation data. Our approach is to consider AES as a Multiple Instance Learning (MIL) task. We show that such models can both predict content scores and localize content by leveraging their sentence-level score predictions. This capability arises despite never having access to annotation training data. Implications are discussed for improving formative feedback and explainable AES models.


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Morphological Reinflection in Context: CU Boulder’s Submission to CoNLLSIGMORPHON 2018 Shared Task
Ling Liu | Ilamvazhuthy Subbiah | Adam Wiemerslage | Jonathan Lilley | Sarah Moeller
Proceedings of the CoNLL–SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection

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Phonological Features for Morphological Inflection
Adam Wiemerslage | Miikka Silfverberg | Mans Hulden
Proceedings of the Fifteenth Workshop on Computational Research in Phonetics, Phonology, and Morphology

Modeling morphological inflection is an important task in Natural Language Processing. In contrast to earlier work that has largely used orthographic representations, we experiment with this task in a phonetic character space, representing inputs as either IPA segments or bundles of phonological distinctive features. We show that both of these inputs, somewhat counterintuitively, achieve similar accuracies on morphological inflection, slightly lower than orthographic models. We conclude that providing detailed phonological representations is largely redundant when compared to IPA segments, and that articulatory distinctions relevant for word inflection are already latently present in the distributional properties of many graphemic writing systems.


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Data Augmentation for Morphological Reinflection
Miikka Silfverberg | Adam Wiemerslage | Ling Liu | Lingshuang Jack Mao
Proceedings of the CoNLL SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection