Improving Opinion Mining Through Automatic Prompt Construction
Subject Areas : Natural Language Processing
Arash Yousefi Jordehi
1
,
Mahsa Hosseini Khasheh Heyran
2
,
Saeed Ahmadnia
3
,
Seyed Abolghassem Mirroshandel
4
*
,
Owen Rambow
5
1 - Faculty of Computer Engineering, University of Guilan, Rasht, Iran
2 - Faculty of Computer Engineering, University of Guilan, Rasht, Iran
3 - Faculty of Computer Engineering, University of Guilan, Rasht, Iran
4 - Faculty of Computer Engineering, University of Guilan, Rasht, Iran
5 - Computer Science Department, Stony Brook university, New York, USA
Keywords: Opinion Mining/Sentiment Analysis, Statistical and Machine Learning Methods, Large Language Models, MPQA, Automatic Prompt Construction,
Abstract :
Opinion mining is a fundamental task in natural language processing. This paper focuses on extracting opinion structures, which are triplets of text representing an opinion, a part of text involving an opinion role, and a relation between opinion and role. We utilize the T5 generative transformer for this purpose. We also adopt a multi-task learning approach inspired by successful previous studies to enhance performance. Nevertheless, the success of generative models heavily relies on the prompts provided in the input, as prompts customize the task at hand. To eliminate the need for human-based prompt design and improve performance, we propose an approach called Automatic Prompt Construction which involves fine-tuning. Our proposed method is fully compatible in multi-task learning as we did so in our investigations. We run a comprehensive set of experiments on Multi-Perspective Question Answering (MPQA) 2.0, which is a commonly utilized benchmark dataset in this domain. By combining automatic prompt construction with multi-task learning, we observe a considerable performance boost. Besides, we develop a new method that re-use a model from one setting of problem to improve other model in another setting as a Transfer Learning application. Our results on the MPQA represent a new state-of-the-art and provide clear directions for future work.
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