Automatic Short Answer Grading With SemSpace Sense Vectors and MaLSTM

Tulu C. N., Ozkaya O., ORHAN U.

IEEE ACCESS, cilt.9, ss.19270-19280, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 9
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1109/access.2021.3054346
  • Dergi Adı: IEEE ACCESS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.19270-19280
  • Anahtar Kelimeler: Semantics, Natural language processing, Benchmark testing, Long short term memory, Deep learning, Task analysis, Learning systems, Automatic short answer grading, MaLSTM, semspace sense vectors, synset based sense embedding, sentence similarity, OPEN-ENDED QUESTIONS
  • Çukurova Üniversitesi Adresli: Evet

Özet

Automatic assessment of exams is widely preferred by educators than multiple-choice exams because of its efficiency in measuring student performance, lack of subjectivity when evaluating student response, and faster evaluation time than the time consuming manual evaluation. In this study, a new approach for the Automatic Short Answer Grading (ASAG) is proposed using MaLSTM and the sense vectors obtained by SemSpace, a synset based sense embedding method built leveraging WordNet. Synset representations of the Student's answers and reference answers are given as input into parallel LSTM architecture, they are transformed into sentence representations in the hidden layer and the vectorial similarity of these two representation vectors are computed with Manhattan Similarity in the output layer. The proposed approach has been tested using the Mohler ASAG dataset and successful results are obtained in terms of Pearson (r) correlation and RMSE. Also, the proposed approach has been tested as a case study using a specific dataset (CU-NLP) created from the exam of the "Natural Language Processing" course in the Computer Engineering Department of Cukurova University. And it has achieved a successful correlation. The results obtained in the experiments show that the proposed system can be used efficiently and effectively in context-dependent ASAG tasks.