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An important step towards enabling English language learners to improve theirconversational speaking proficiency involves automated scoring of multipleaspects of interactional competence and subsequent targeted feedback. Thispaper builds on previous work in this direction to investigate multiple neuralarchitectures -- recurrent, attention and memory based -- along withfeature-engineered models for the automated scoring of interactional and topicdevelopment aspects of text dialog data. We conducted experiments on aconversational database of text dialogs from human learners interacting with acloud-based dialog system, which were triple-scored along multiple dimensionsof conversational proficiency. We find that fusion of multiple architecturesperforms competently on our automated scoring task relative to expertinter-rater agreements, with (i) hand-engineered features passed to a supportvector learner and (ii) transformer-based architectures contributing mostprominently to the fusion.

Exploring Recurrent, Memory and Attention Based Architectures for Scoring Interactional Aspects of Human-Machine Text Dialog