We developed a robust domain selection method and verified its extensibility. An issue in domain selection is its robustness against out-of-grammar utterances. It is essential to generate correct system responses because such utterances often cause domain selection errors. We therefore integrated the topic estimation results and the dialogue history to construct a robust domain classifier. Another issue is that domain selection should be performed within an extensible framework, because the system is often modified and extended. That is, the classifier should still have high performance without reconstructing it after adding new domains. The extensibility of our method was not experimentally verified yet, because it requires a lot of effort to collect new dialogue data after extending the system. Therefore, we verified extensibility without collecting new data. We constructed the classifier by leaving out some domains in the dialogue data and then evaluated its accuracy as the classifier for the data where the left-out domains were virtually added.
|ジャーナル||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|出版ステータス||Published - 2008 12月 1|
|イベント||INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia|
継続期間: 2008 9月 22 → 2008 9月 26
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