FinBERT-MRC: Revolutionizing Financial Question Answering with Advanced NLP
Financial named entity recognition using BERT under the machine reading comprehension paradigm. Yeah. Somehow I am still thinking about my investment agent.
The naive approach when working with financial news is to calculate sentiments. Positive, Neutral, Negative. Usually along those axes. If you had followed the advice of a sentiment signal in November 2022, you would have likely never invested in Meta and Nvidia, arguably some of the best-performing investments of the last 12 months. So what else can be done to bring order into a whirlwind of sometimes valuable information in a highly regulated environment with dire consequences in case of errors?
In this Paper Review, I am trying to answer this question by discussing the highly under-reported FinBert-MRC paper and its implications.
Project Goal: Find an effective way to automatically retrieve financial entities (e.g., financial institutions, ratios, dates, currencies, and companies) in given texts for the purpose of building knowledge graphs and other structured knowledge formats.
Problem: Every day the world produces more content than ever before with a negative effect on a hypothetical signal/noise ratio. What information matters to truly understand an investment? Semantic context matters here more than in any other domain.
Proposed Solution: Defining named-entity recognition in finance as a machine reading comprehension enhanced sequence labeling task.
Opinion: This project aims to solve a really important subject. The approach to combine machine reading comprehension and BERT labeling makes sense. However, I don’t think that the dataset from China can be applied to other markets easily. Since I have a similar dataset I am currently working on I might have a good shot at evaluating this.
Let’s dive in.
Before we jump into the applications of the paper, let me expand more on the problem that the project team is trying to solve.