ISLab at the NTCIR-17 QA Lab-PoliInfo-4: Models for Automatically Identifying Politicians' Stances on Bills

This paper aims to design a model that can determine whether the politician's stance is approved or disapproved the bill based on the politician's utterance on a specific bill in the parliament. This study proposed two frameworks for determining the stance in utterances. The first framework involves concatenating BERT model with Bi-LSTM model to form a comprehensive decision-making model while the second framework is concatenating Curie model with ChatGPT model. This paper used the dataset provided by Stance Classification 2 task in NTCIR-17 for model training and testing, and GPT-based model this paper proposed achieved an accuracy of 0.932.