LIPI at the NTCIR-17 FinArg-1 Task: Using Pre-trained Language Models for Comprehending Financial Arguments

Comprehending arguments from financial texts helps investors in making data driven decisions. The FinArg tasks of NTCIR-17 deal with mining arguments related to finance from Research Reports, Earnings Conference Calls, and Social Media. In this paper, we describe our team's approach to solve the three such problems - Argument Unit Classification, Argument Relation Detection & Classification, and Identifying Attack and Support Argumentative Relations. We obtained best performance using pre-trained language models (like BERT-SEC and FinBERT) and cross-encoder architecture.