NumClaim: Investor's Fine-grained Claim Detection

Abstract

The goal of claim detection in argument mining is to sort out the key points from a long narrative. In this paper, we design a novel task for argument mining in the financial domain, and provide an expert-annotated dataset, NumClaim, for the proposed task. Based on the statistics, we discuss the differences between the claims in other datasets and the claims of the investors in NumClaim. With the ablation analysis, we show that encoding numeral and co-training with the auxiliary task of the numeral understanding, i.e., the category classification task, can improve the performance of the proposed task under different neural network architectures. The annotations in the NumClaim is published for academic usage under the CC BY-NC-SA 4.0 license.