Generative Stock Question Answering

TL;DR

This paper introduces a memory-augmented encoder-decoder model for stock question answering, capable of generating natural language answers that incorporate numerical data, and demonstrates its effectiveness on a large-scale dataset.

Contribution

It proposes a novel memory-augmented neural architecture with number understanding mechanisms for StockQA, and provides a comprehensive dataset and analysis of model performance.

Findings

Hybrid word-character model with number processing performs best.

44.8% of answers still generic, improved by retrieval-generation hybrid.

Large-scale dataset of 180K StockQA instances created.

Abstract

We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing action recommendations to stocks upon user's requests. StockQA is quite different from previous QA tasks since (1) the answers in StockQA are natural language sentences (rather than entities or values) and due to the dynamic nature of StockQA, it is scarcely possible to get reasonable answers in an extractive way from the training data; and (2) StockQA requires properly analyzing the relationship between keywords in QA pair and the numerical features of a stock. We propose to address the problem with a memory-augmented encoder-decoder architecture, and integrate different mechanisms of number understanding and generation, which is a critical component of StockQA. We build a large-scale dataset containing over 180K StockQA instances, based on which various technique combinations are extensively studied and compared. Experimental results show that a hybrid word-character model with separate character components for number processing, achieves the best performance. By analyzing the results, we found that 44.8% of answers generated by our best model still suffer from the generic answer problem, which can be alleviated by a straightforward hybrid retrieval-generation model.