Automatic Numerical Question Answering on Table using BERT-GNN

The table base numerical question-answering task requires a mechanism to understand the relation between table content and numbers (present in table and question). It also needs an efficient method to address complex reasoning on table context. Most of the existing approaches in the natural language processing technology address the context-based questions on the table but fail to address the numerical reasoning part. They are also built on a large search database, which makes it challenging to use them in multiple domains. These approaches use pre-trained models like BERT to perform context encoding of a complete table. Hence these models fail when a large table is provided as input, as full table encoding is a very resource and time-consuming task. In this paper, a framework is proposed to answer questions on the table with numerical reasoning. This framework uses a context-snapshot mechanism to filter irrelevant table rows before tokenizing the table content. The filtered context and tokenized question are converted into vector representation using a pre-trained BERT model. This proposed model finds the correlation between the tokenized context-snapshot and numbers in question using graph neural networks. Further, it uses a feed-forward neural network to perform the numerical operation to compute the answer. The model is trained and evaluated on WikiTableQuestions datasets, shows a promising result.