Sequential Attention-based Network for Noetic End-to-End Response Selection

9 Jan 2019Qian ChenWen Wang

The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context. This paper describes our systems that are ranked the top on both datasets under this challenge, one focused and small (Advising) and the other more diverse and large (Ubuntu)... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Conversational Response Selection DSTC7 Ubuntu Sequential Attention-based Network 1-of-100 Accuracy 64.5% # 4
Conversational Response Selection Ubuntu Dialogue (v1, Ranking) ESIM [email protected] 0.796 # 3
[email protected] 0.894 # 3
[email protected] 0.975 # 4