A Dataset for Research on Short-Text Conversations
A Dataset for Research on Short-Text Conversations
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发表时间：
2013-10
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通讯作者：
Hao Wang;Zhengdong Lu;Hang Li;Enhong Chen
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作者：
Hao Wang;Zhengdong Lu;Hang Li;Enhong Chen
自然语言对话被广泛认为是一个非常困难的问题，通常使用基于规则或基于学习的模型来解决。在本文中，我们提出了一种基于检索的短文本对话自动响应模型，以利用社交媒体上可用的大量短对话实例。为此，我们引入了基于新浪微博（中国流行的微博服务）的真实实例的短文本对话数据集，该数据集即将向公众发布。该数据集为寻找给定短文本的自然且相关的简短响应的研究提供了丰富的实例集合，并且对于对话模型的训练和测试都很有用。该数据集由自然形成的对话、手动标记的数据和大量候选响应组成。我们的初步实验表明，当与数据集中的丰富实例相结合时，简单的基于检索的对话模型表现得相当好。
Natural language conversation is widely regarded as a highly difficult problem, which is usually attacked with either rule-based or learning-based models. In this paper we propose a retrieval-based automatic response model for short-text conversation, to exploit the vast amount of short conversation instances available on social media. For this purpose we introduce a dataset of short-text conversation based on the real-world instances from Sina Weibo (a popular Chinese microblog service), which will be soon released to public. This dataset provides rich collection of instances for the research on finding natural and relevant short responses to a given short text, and useful for both training and testing of conversation models. This dataset consists of both naturally formed conversations, manually labeled data, and a large repository of candidate responses. Our preliminary experiments demonstrate that the simple retrieval-based conversation model performs reasonably well when combined with the rich instances in our dataset.