Authors
Swarnadeep Saha, Tejas I Dhamecha, Smit Marvaniya, Renuka Sindhgatta, Bikram Sengupta
Publication date
2018/6/27
Conference
AIED
Pages
503-517
Publisher
Springer, Cham
Description
Automatic short answer grading for Intelligent Tutoring Systems has attracted much attention of the researchers over the years. While the traditional techniques for short answer grading are rooted in statistical learning and hand-crafted features, recent research has explored sentence embedding based techniques. We observe that sentence embedding techniques, while being effective for grading in-domain student answers, may not be best suited for out-of-domain answers. Further, sentence embeddings can be affected by non-sentential answers (answers given in the context of the question). On the other hand, token level hand-crafted features can be fairly domain independent and are less affected by non-sentential forms. We propose a novel feature encoding based on partial similarities of tokens (Histogram of Partial Similarities or HoPS), its extension to part-of-speech tags (HoPSTags) and question …
Total citations
20182019202020212022202320241119819205
Scholar articles
S Saha, TI Dhamecha, S Marvaniya, R Sindhgatta… - Artificial Intelligence in Education: 19th International …, 2018