Name
Abstract | ||
|---|---|---|
Detecting out-of-domain (OOD) intents is critical in a task-oriented dialog system. Existing methods rely heavily on extensive manually labeled OOD samples and lack robustness. In this paper, we propose an efficient adversarial attack mechanism to augment hard OOD samples and design a novel generative distance-based classifier to detect OOD samples instead of a traditional threshold-based discriminator classifier. Experiments on two public benchmark datasets show that our method can consistently outperform the baselines with a statistically significant margin. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413908 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Intent Detection, Out-of-Domain, Adversarial Attack, Gaussian Discriminant Analysis | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyuan Zeng | 1 | 0 | 2.03 |
| Hong Xu | 2 | 0 | 1.69 |
| Keqing He | 3 | 0 | 3.04 |
| Yuanmeng Yan | 4 | 0 | 4.06 |
| Sihong Liu | 5 | 0 | 1.35 |
| Zijun Liu | 6 | 8 | 3.33 |
| Weiran Xu | 7 | 210 | 43.79 |