Topic detection based on dialogue history
Takayuki Nakata, Shinichi Ando, Akitoshi Okumura · 2002
In this paper, we propose a topic detection method using a dialogue history for a speech translator. The method uses a k-nearest neighbor method for the algorithm, automatically clusters target topics into smaller topics grouped by similarity, and incorporates dialogue history weighted in terms of time to detect and track topics on spoken phrases. From the evaluation of detection performance using test data comprised of realistic spoken dialogue, the method has shown to perform better with clustering incorporated, and when combined with dialogue history of three sentences, gives detection accuracy of 72.1%.