Papers › Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented...
Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems
Jia-Chen Gu, Tianda Li, Quan Liu, Xiaodan Zhu, Zhen-Hua Ling, Yu-Ping Ruan
The NOESIS II challenge, as the Track 2 of the 8th Dialogue System Technology Challenges (DSTC 8), is the extension of DSTC 7. This track incorporates new elements that are vital for the creation of a deployed task-oriented dialogue system. This paper describes our systems that are evaluated on all subtasks under this challenge. We study the problem of employing pre-trained attention-based network for multi-turn dialogue systems. Meanwhile, several adaptation methods are proposed to adapt the pre-trained language models for multi-turn dialogue systems, in order to keep the intrinsic property of dialogue systems. In the released evaluation results of Track 2 of DSTC 8, our proposed models ranked fourth in subtask 1, third in subtask 2, and first in subtask 3 and subtask 4 respectively.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conversation Disentanglement | irc-disentanglement | BERT + BiLSTM | F | 46.8 | #1 of 5 | Archive leaderboard | report |
| Conversation Disentanglement | irc-disentanglement | BERT + BiLSTM | P | 44.3 | #1 of 5 | Archive leaderboard | report |
| Conversation Disentanglement | irc-disentanglement | BERT + BiLSTM | R | 49.6 | #1 of 5 | Archive leaderboard | report |
| Conversation Disentanglement | irc-disentanglement | BERT + BiLSTM | VI | 93.3 | #1 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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