Name
Title | ||
|---|---|---|
N-best Response-based Analysis of Contradiction-awareness in Neural Response Generation Models. |
Abstract | ||
|---|---|---|
Avoiding the generation of responses that contradict the preceding context is a significant challenge in dialogue response generation. One feasible method is post-processing, such as filtering out contradicting responses from a resulting n-best response list. In this scenario, the quality of the n-best list considerably affects the occurrence of contradictions because the final response is chosen from this n-best list. This study quantitatively analyzes the contextual contradiction-awareness of neural response generation models using the consistency of the n-best lists. Particularly, we used polar questions as stimulus inputs for concise and quantitative analyses. Our tests illustrate the contradiction-awareness of recent neural response generation models and methodologies, followed by a discussion of their properties and limitations. |
Year | Venue | DocType |
|---|---|---|
2022 | SIGdial Meetings (SIGDIAL) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiki Sato | 1 | 0 | 0.68 |
| Reina Akama | 2 | 2 | 2.45 |
| Hiroki Ouchi | 3 | 18 | 8.08 |
| Ryoko Tokuhisa | 4 | 0 | 1.01 |
| Jun Suzuki | 5 | 55 | 10.39 |
| Kentaro Inui | 6 | 0 | 3.04 |