Training IBM Watson using Automatically Generated Question-Answer Pairs

TL;DR

This paper explores the use of a large-scale automatically generated question-answer dataset to efficiently train IBM Watson, demonstrating its effectiveness and complementarity to manual data.

Contribution

First to investigate large-scale auto-generated question-answer pairs for training IBM Watson, improving training efficiency and accuracy.

Findings

Auto-generated dataset effectively trains Watson

Complementary to manual question-answer pairs

Enhances training efficiency and accuracy

Abstract

IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and significantly limits the efficiency of Watson's training. Recently, a large-scale dataset of over 30 million question-answer pairs was reported. Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy. According to our experiments, using this auto-generated dataset was effective for training Watson, complementing manually crafted question-answer pairs. To the best of the authors' knowledge, this work is the first attempt to use a large-scale dataset of automatically generated question-answer pairs for training IBM Watson. We anticipate that the insights and lessons obtained from our experiments will be useful for researchers who want to expedite Watson training leveraged by automatically generated question-answer pairs.