Papers by Jeong-Gwan Lee
End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2 (2020.acl-main)
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| Challenge: | End-to-end dialogue systems with monolithic neural architecture are often trained with input-output utterances without taking into account the entire annotations available in the corpus. |
| Approach: | They propose an end-to-end neural architecture for goal-oriented dialogue systems that addresses both challenges . they propose a modular architecture where modules are optimized individually . |
| Outcome: | The proposed system achieved the top position in the human evaluation task . it is based on a neural architecture that can be integrated with external systems . |