Scoring of Response Based on Suitability of Dialogue-act and Content Similarity
Scoring of Response Based on Suitability of Dialogue-act and Content Similarity
复制标题
基于对话行为适宜性和内容相似度的回答评分
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Masahiro Araki
中科院分区：
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作者：
Sota Matsumoto;Masahiro Araki
我们提出了一种方法，对大型短文本会话（STC）数据库中的候选话语进行评分，以选择那些用于对新给定话语的合适响应的候选话语。根据对话行为的适宜性和内容相似性对候选话语进行评估。通过学习存储库中频繁出现的对话行为对的趋势来实现对对话行为适用性的评估。此外，我们利用LDA和IDF的主题向量余弦相似度计算了话语之间的内容相似度。将这些值相乘，那些在功能和内容上都适合的候选词就会得到高分。实验评估结果表明，对于内容相似度，增加IDF的权重会产生更好的准确性。
We present an approach to scoring candidate utterances in a large repository of short text conversation (STC) data to select those to be used as a suitable response to a newly given utterance. Candidate utterances are evaluated based on the suitability of a dialogue-act and the content similarity. The estimation of the suitability of a dialogue-act is implemented by learning the trend of a dialogue-act pair that frequently appears in the repository. Also, we calculated the content similarity between utterances by means of the cosine similarity of topic vectors using LDA and IDF. By multiplying these values, those candidates which are suitable in terms of function and content attain a high score. As a result of the experimental evaluation, for content similarity, it was found that increasing the weighting of the IDF produces a better accuracy.