Paper: Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech

ACL ID P12-1006
Title Automated Essay Scoring Based on Finite State Transducer: towards ASR Transcription of Oral English Speech
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Conventional Automated Essay Scoring (AES) measures may cause severe problems when directly applied in scoring Automatic Speech Recognition (ASR) transcription as they are error sensitive and unsuitable for the characteristic of ASR transcription. Therefore, we introduce a framework of Finite State Transducer (FST) to avoid the shortcomings. Compared with the Latent Semantic Analysis with Support Vector Regression (LSA-SVR) method (stands for the conventional measures), our FST method shows better performance especially towards the ASR transcription. In addition, we apply the synonyms similarity to expand the FST model. The final scoring performance reaches an acceptable level of 0.80 which is only 0.07 lower than the correlation (0.87) between human raters.