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

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