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Investigating the effect of auxiliary objectives for the automated grading of learner English speech transcriptions.

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Hannah Craighead, Andrew Caines, Paula Buttery, Helen Yannakoudakis

Original languageEnglish
Title of host publication Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
PublisherAssociation for Computational Linguistics (ACL)
Accepted/In press3 Apr 2020


King's Authors


We address the task of automatically grading the language proficiency of spontaneousspeech based on textual features from automatic speech recognition transcripts. Motivated by recent advances in multi-task learning, we develop neural networks trained in a multi-task fashion that learn to predict the proficiency level of non-native English speakers by taking advantage of inductive transfer between the main task (grading) and auxiliary prediction tasks: morpho-syntactic labeling,language modeling, and native language identification (L1). We encode the transcriptions with both bi-directional recurrent neural networks and with bi-directional representations from transformers, compare against a feature-rich baseline, and analyse performance at different proficiency levels and with transcriptions of varying error rates. Our best performance comes from a transformer encoder with L1 prediction as an auxiliary task. We discuss areas for improvement and potential applications for text-only speech scoring.

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