| Presentation # | 6 |
| Session: | Dialogue |
| Session Time: | Thursday, December 20, 10:00 - 12:00 |
| Presentation Time: | Thursday, December 20, 10:00 - 12:00 |
| Presentation: |
Poster
|
| Topic: |
Spoken dialog systems: |
| Paper Title: |
OUT-OF-DOMAIN SLOT VALUE DETECTION FOR SPOKEN DIALOGUE SYSTEMS WITH CONTEXT INFORMATION |
| Authors: |
Yuka Kobayashi; Toshiba Corporation | | |
| | Takami Yoshida; Toshiba Corporation | | |
| | Kenji Iwata; Toshiba Corporation | | |
| | Hiroshi Fujimura; Toshiba Corporation | | |
| | Masami Akamine; Toshiba Corporation | | |
| Abstract: |
This paper proposes an approach to detecting out-of-domain slot values from user utterances in spoken dialogue systems based on contexts. The approach detects keywords of slot values from utterances and consults domain knowledge (i.e., an ontology) to check whether the keywords are out-of-domain. This can prevent the systems from responding improperly to user requests. We use a Recurrent Neural Network (RNN) encoder-decoder model and propose a method that uses only in-domain data. The method replaces word embedding vectors of the keywords corresponding to slot values with random vectors during training of the model. This allows using context information. The model is robust against over-fitting problems because it is independent of the slot values of the training data. Experiments show that the proposed method achieves a 65% gain in F1 score relative to a baseline model and a further 13 percentage points by combining with other methods. |