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Presentation #10
Session:Corpora and Evaluation Methodologies
Session Time:Wednesday, December 19, 13:30 - 15:30
Presentation Time:Wednesday, December 19, 13:30 - 15:30
Presentation: Poster
Topic: Evaluation methodologies: Educational:
Paper Title: A PROMPT-AWARE NEURAL NETWORK APPROACH TO CONTENT-BASED SCORING OF NON-NATIVE SPONTANEOUS SPEECH
Authors: Yao Qian; Educational Testing Service 
 Rutuja Ubale; Educational Testing Service 
 Matthew Mulholland; Educational Testing Service 
 Keelan Evanini; Educational Testing Service 
 Xinhao Wang; Educational Testing Service 
Abstract: We present a neural network approach to the automated assessment of non-native spontaneous speech in a listen and speak task. An attention-based Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is used to learn the relations (scoring rubrics) between the spoken responses and their assigned scores. Each prompt (listening material) is encoded as a vector in a low-dimensional space and then employed as a condition of the inputs of the attention LSTM-RNN. The experimental results show that our approach performs as well as the strong baseline of a Support Vector Regressor (SVR) using content-related features, i.e., a correlation of r = 0.806 with holistic proficiency scores provided by humans, without doing any feature engineering. The prompt-encoded vector improves the discrimination between the high-scoring sample and low-scoring sample, and it is more effective in grading responses to unseen prompts, which have no corresponding responses in the training set.