Towards Improving Predictive AAC using Crowdsourced Dialogues and Partner Context
Towards Improving Predictive AAC using Crowdsourced Dialogues and Partner Context
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使用众包对话和合作伙伴环境改进预测 AAC
DOI：
10.1145/3132525.3134814
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
K. Vertanen
中科院分区：
文献类型：
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作者：
K. Vertanen
增强和替代通信（AAC）设备通常依赖于语言模型来帮助进行预测或消除用户输入的歧义。我们研究如何提高双边会话对话中的预测。我们收集并分享一个新的众包日常对话语料库。我们展示了基于递归神经网络的语言模型如何在这些对话上优于N-gram模型。我们证明了进一步的收益是可能的，使用从AAC用户的通信伙伴获得的文本，即使该文本是部分或包含错误。
Augmentative and Alternative Communication (AAC) devices typically rely on a language model to help make predictions or disambiguate user input. We investigate how to improve predictions in two-sided conversational dialogues. We collect and share a new corpus of crowdsourced everyday dialogues. We show how language models based on recurrent neural networks outperform N-gram models on these dialogues. We demonstrate further gains are possible using text obtained from an AAC user's communication partner, even when that text is partial or contains errors.