@InProceedings{yoon-EtAl:2018:N18-3,
  author    = {Yoon, Su-Youn  and  Cahill, Aoife  and  Loukina, Anastassia  and  Zechner, Klaus  and  Riordan, Brian  and  Madnani, Nitin},
  title     = {Atypical Inputs in Educational Applications},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans - Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {60--67},
  abstract  = {In large-scale educational assessments, the use of automated scoring has recently become quite common. While the majority of student responses can be processed and scored without difficulty, there are a small number of responses that have atypical characteristics that make it difficult for an automated scoring system to assign a correct score. We describe a pipeline that detects and processes these kinds of responses at run-time. We present the most frequent kinds of what are called non-scorable responses along with effective filtering models based on various NLP and speech processing technologies. We give an overview of two operational automated scoring systems ---one for essay scoring and one for speech scoring--- and describe the filtering models they use. Finally, we present an evaluation and analysis of filtering models used for spoken responses in an assessment of language proficiency.},
  url       = {http://www.aclweb.org/anthology/N18-3008}
}