Dialogue Context Modelling for Action Item Detection: Solution for ICASSP 2023 Mug Challenge Track 5
Jie Huang, Xiachong Feng, Yangfan Ye, Liang Zhao, Xiaocheng Feng, Bing Qin, Ting Liu
Abstract
Action item detection aims at recognizing sentences containing information about actionable tasks, which can help people quickly grasp core tasks in the meeting without going through the redundant meeting contents. Therefore, in this paper, we thoroughly describe our carefully designed solution for the Action Item Detection Track of the General Meeting Understanding and Generation (MUG) challenge in the ICASSP 2023 Signal Processing Grand Challenge. Specifically, we systematically analyze the task instances provided by MUG and find that the key ingredient for successful action item detection is leveraging the dialogue context information into consideration. To this end, we design a simple and effective method for modelling context and utterance information concurrently. The experimental results show our method achieves remarkable improvements over baseline models, with an absolute increase of 0.62 of the F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score on the validation set. The stable generalizability of our method is further verified by our score on the final test set<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
BibTeX
@inproceedings{icassp2023_dialoguecontextm,
title = {Dialogue Context Modelling for Action Item Detection: Solution for ICASSP 2023 Mug Challenge Track 5},
author = {Jie Huang and Xiachong Feng and Yangfan Ye and Liang Zhao and Xiaocheng Feng and Bing Qin and Ting Liu},
booktitle = {ICASSP 2023},
year = {2023}
}