Action Item Detection in Meetings Using Pretrained Transformers

Detecting the action items that were agreed upon during a meeting has important practical applications. But, extremely sparse positive labels, noisy annotations, and small datasets limited the improvement in this task via hand-crafted features techniques. Given the breakthrough performance demonstrated by pretrained transformer-based models in a wide variety of NLP tasks, such as BERT [1] and ETC [2], we revisit this task using these modern techniques. We empirically show how these modelling techniques advance the state-of-the-art on action item detection for the ICSI simulated meeting corpus [3] by 75% and establish a baseline for the action item detection from the AMI [4] meeting corpus. We show that these models are competitive on the related ICSI MRDA classification problem. In order to push the performance even further, we re-evaluate the task definition for action item detection, drawing upon the similarities with the span boundary detection realm. We propose the use of Generalized Hamming Distance ghd as an alternative evaluation. We hope to motivate further interest into the action item detection task by the community.