Overinformative Question Answering by Humans and Machines

Understand

When faced with a polar question, speakers often provide overinformative answers going beyond a simple "yes" or "no".

• But what principles guide the selection of additional information? In this paper, we provide experimental evidence from two studies suggesting that overinformativeness in human answering is driven by considerations of relevance to the questioner's goals which they flexibly adjust given the functional context in which the question is uttered.
• We take these human results as a strong benchmark for investigating question-answering performance in state-of-the-art neural language models, conducting an extensive evaluation on items from human experiments.
• We find that most models fail to adjust their answering behavior in a human-like way and tend to include irrelevant information.