基于多通道卷积神经网络的跨语言对话状态跟踪

Hongjie Shi, Takashi Ushio, M. Endo, K. Yamagami, Noriaki Horii
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引用次数: 34

摘要

第五次对话状态跟踪挑战(DSTC5)引入了一个新的跨语言对话状态跟踪场景,参与者被要求基于英语训练语料库构建自己的跟踪器,同时使用未标记的中文语料库对其进行评估。尽管数据集中提供了计算机生成的中英文语料库翻译,但这些翻译包含错误,并且不小心使用它们很容易损害构建的跟踪器的性能。为了解决这个问题,我们提出了一种多通道卷积神经网络(CNN)架构,其中我们将英语和中文作为一个CNN模型的不同输入通道。在对DSTC5的评估中,我们发现这种多通道架构可以有效地提高对翻译错误的鲁棒性。此外,我们的DSTC5方法纯粹是基于机器学习的,不需要关于目标语言的先验知识。我们认为这是在跨语言上下文中构建跟踪器的理想属性,因为不是每个开发人员都熟悉这两种语言。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A multichannel convolutional neural network for cross-language dialog state tracking
The fifth Dialog State Tracking Challenge (DSTC5) introduces a new cross-language dialog state tracking scenario, where the participants are asked to build their trackers based on the English training corpus, while evaluating them with the unlabeled Chinese corpus. Although the computer-generated translations for both English and Chinese corpus are provided in the dataset, these translations contain errors and careless use of them can easily hurt the performance of the built trackers. To address this problem, we propose a multichannel Convolutional Neural Networks (CNN) architecture, in which we treat English and Chinese language as different input channels of one single CNN model. In the evaluation of DSTC5, we found that such multichannel architecture can effectively improve the robustness against translation errors. Additionally, our method for DSTC5 is purely machine learning based and requires no prior knowledge about the target language. We consider this a desirable property for building a tracker in the cross-language context, as not every developer will be familiar with both languages.
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