Amazon details AI that answers questions more reliably

Could natural language models improve their ability to answer questions on the fly? That's what a team of Amazon researchers set out to answer in a study scheduled to be presented at the 2020 Association for the Advancement of Artificial Intelligence in New York. They posit a method for adapting models based on Google's Transformer architecture -- which is particularly good at learning long-range dependencies among input data (such as the semantic and syntactic relationships between individual words of a sentence) -- to address the problem of answer selection. The team says that in tests on a benchmark data set, their proposed model demonstrated a 10% absolute improvement in mean average precision (which measures the quality of a sorted list of answers according to the correctness of the ranking) over the previous state-of-the-art answer selection model, achieving an error rate reduction of 50%. The approach -- Transfer and Adapt, or TANDA -- was first proposed late last year but has since been refined.