Exploring Recurrent, Memory and Attention Based Architectures for Scoring Interactional Aspects of Human-Machine Text Dialog
Vikram Ramanarayanan,
Matthew Mulholland,
Debanjan Ghosh
Abstract:An important step towards enabling English language learners to improve their conversational speaking proficiency involves automated scoring of multiple aspects of interactional competence and subsequent targeted feedback. This paper builds on previous work in this direction to investigate multiple neural architectures -recurrent, attention and memory based -along with feature-engineered models for the automated scoring of interactional and topic development aspects of text dialog data. We conducted experiment… Show more
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