Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection

• URL: http://arxiv.org/abs/2004.09036v4
• Date: Mon, 17 Aug 2020 07:08:36 GMT
• Title: Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
• Authors: Yefei Zha, Ruobing Li, Hui Lin
• Abstract summary: We propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts. We introduce a new model, Gated Convolutional Bidirectional Attention-based Model (GCBiA), which applies bi-attention mechanism and convolutions to extract topic words of prompts and key-phrases of responses.
• Score: 10.321357718530473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Off-topic spoken response detection, the task aiming at predicting whether a response is off-topic for the corresponding prompt, is important for an automated speaking assessment system. In many real-world educational applications, off-topic spoken response detectors are required to achieve high recall for off-topic responses not only on seen prompts but also on prompts that are unseen during training. In this paper, we propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts. We introduce a new model, Gated Convolutional Bidirectional Attention-based Model (GCBiA), which applies bi-attention mechanism and convolutions to extract topic words of prompts and key-phrases of responses, and introduces gated unit and residual connections between major layers to better represent the relevance of responses and prompts. Moreover, a new negative sampling method is proposed to augment training data. Experiment results demonstrate that our novel approach can achieve significant improvements in detecting off-topic responses with extremely high on-topic recall, for both seen and unseen prompts.

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