A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)
Abstract
Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.
Cite
Text
Jain et al. "A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7180Markdown
[Jain et al. "A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/jain2020aaai-multi/) doi:10.1609/AAAI.V34I10.7180BibTeX
@inproceedings{jain2020aaai-multi,
title = {{A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)}},
author = {Jain, Minni and Leekha, Maitree and Goswami, Mononito},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13817-13818},
doi = {10.1609/AAAI.V34I10.7180},
url = {https://mlanthology.org/aaai/2020/jain2020aaai-multi/}
}