论文标题
封闭式卷积双向注意模型，用于非主题口语响应检测
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
论文作者
论文摘要
主题响应检测是旨在预测响应是否偏离相应提示的任务，对于自动说话评估系统很重要。在许多现实世界中的教育应用中，不仅在可见的提示，而且在培训期间看不见的提示下，需要高度召回主题响应探测器才能获得高额响应。在本文中，我们提出了一种新颖的方法，可以在可见和看不见的提示中进行高主召回的主题响应检测。我们介绍了一种新模型，封闭式卷积双向注意模型（GCBIA），该模型采用双重意见机制和卷积来提取响应提示和键短语的主题单词，并引入了主要层之间的封闭式单位和残留联系，以更好地表示响应和提示的相关性。此外，提出了一种新的负面抽样方法来增强培训数据。实验结果表明，我们的新方法可以在检测外主响应的情况下以极高的主题召回和看不见的提示来取得重大改进。
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.
