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IBM's Debating AI Just Got a Lot Closer to Being a Useful Tool


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The IBM Debater system taking part in a debate at the University of Cambridge last year.

IBM upgraded the neural networks used by its Project Debater system, to improve the quality of evidence the argument-mining system uncovers.

Credit: IBM Research

IBM upgraded the neural networks used by its Project Debater system to improve the quality of evidence the argument-mining system uncovers.

One new add-on for the debating system is BERT (Bidirectional Encoder Representations from Transformers), a network designed by Google for natural language processing and answering queries.

IBM Research scientists trained the AI on 400 million documents from the LexisNexis database, providing a natural language dataset of roughly 10 billion sentences; the researchers combined the dataset with claims about several hundred different topics, then had crowdsourced workers label the sentences based on the quality of their evidence for or against specific claims.

A supervised learning algorithm digested this data, allowing BERT to manage queries on a wide range of subjects and to yield more relevant sentences compared to previous systems.

Project Debater was 95% accurate for the top 50 sentences across 100 distinct topics, according to IBM researcher Noam Slonim,

From MIT Technology Review
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