A data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language
A data-driven model of explanations for a chatbot that helps to practice conversation in a foreign language
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聊天机器人的数据驱动解释模型，有助于练习外语对话
DOI：
10.18653/v1/w17-5547
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Sviatlana Höhn
中科院分区：
文献类型：
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作者：
Sviatlana Höhn
本文描述了一个聊天机器人的他人发起的自我修复模型，该模型有助于练习外语对话。该模型是使用德语母语者和非德语母语者之间的即时通讯对话语料库开发的。对话分析帮助从少量的例子中创建计算模型。该模型已在一个基于aiml的聊天机器人中得到验证。与典型的基于检索的对话系统不同，这些解释是在运行时从语言数据库生成的。
This article describes a model of other-initiated self-repair for a chatbot that helps to practice conversation in a foreign language. The model was developed using a corpus of instant messaging conversations between German native and non-native speakers. Conversation Analysis helped to create computational models from a small number of examples. The model has been validated in an AIML-based chatbot. Unlike typical retrieval-based dialogue systems, the explanations are generated at run-time from a linguistic database.