论文标题
请求对话框中的错误校正和提取
Error correction and extraction in request dialogs
论文作者
论文摘要
我们提出了一个对话框系统实用程序组件，以获取用户的最后两个话语，并且可以检测最后一个话语是否是对第二个发言的错误校正。如果是，它会根据最后一句话中的误差校正纠正第二个话语，并输出提取的repandum和维修实体对。该组件提供了两个优点，学习了更正的概念，以避免为每个新领域收集更正，并提取reparandum和维修对，这提供了学习的可能性。 对于误差校正，提出了一个序列标记和两个序列方法的序列。对于误差校正检测，也可以使用这三个误差校正方法，此外，我们提出了序列分类方法。可以将一种误差校正检测和一种错误校正方法组合到管道中，也可以训练误差校正方法并端到端使用以避免两个组件。我们修改了Epic-kitchens-100数据集，以评估请求对话框中纠正实体短语的方法。为了进行误差校正检测和校正，我们在合成验证数据上获得了96.40％的精度，而人类创建的现实世界测试数据的准确度为77.81％。
We propose a dialog system utility component that gets the last two utterances of a user and can detect whether the last utterance is an error correction of the second last utterance. If yes, it corrects the second last utterance according to the error correction in the last utterance and outputs the extracted pairs of reparandum and repair entity. This component offers two advantages, learning the concept of corrections to avoid collecting corrections for every new domain and extracting reparandum and repair pairs, which offers the possibility to learn out of it. For the error correction one sequence labeling and two sequence to sequence approaches are presented. For the error correction detection these three error correction approaches can also be used and in addition, we present a sequence classification approach. One error correction detection and one error correction approach can be combined to a pipeline or the error correction approaches can be trained and used end-to-end to avoid two components. We modified the EPIC-KITCHENS-100 dataset to evaluate the approaches for correcting entity phrases in request dialogs. For error correction detection and correction, we got an accuracy of 96.40 % on synthetic validation data and an accuracy of 77.81 % on human-created real-world test data.
