# Pre-Trained and Attention-Based Neural Networks for Building Noetic Task-Oriented Dialogue Systems

arXiv: https://arxiv.org/abs/2004.01940 · HTML: https://mlsanity.com/paper/2004.01940/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Conversation Disentanglement with Bi-Level Contrastive Learning](https://mlsanity.com/paper/2210.15265/) (2022)
- [Zero-Shot Dialogue Disentanglement by Self-Supervised Entangled Response Selection](https://mlsanity.com/paper/2110.12646/) (2021)
- [Disentangling Online Chats with DAG-Structured LSTMs](https://mlsanity.com/paper/2106.09024/) (2021)

## Abstract (arXiv)

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.