Papers with Ubuntu dialogue corpus
Tailored Sequence to Sequence Models to Different Conversation Scenarios (P18-1)
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| Challenge: | Sequence to sequence (Seq2Sequeq) models fail to meet the diverse requirements for different conversation scenarios, such as customer service and chatbot. |
| Approach: | They propose two optimized criteria for Sequence to sequence (Seq2Sequeq) to meet different conversation scenarios, i.e., maximum generated likelihood for specific-requirement scenario, and conditional value-at-risk for diverse-requrement scenarios. |
| Outcome: | The proposed models satisfies diverse requirements for different conversation scenarios and yields better performances than existing models. |