Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning
This addresses the issue of measuring true progress in multi-hop QA for researchers, revealing incremental improvements by reducing dataset artifacts.
The paper tackles the problem of models exploiting dataset artifacts in multi-hop question-answering, showing that only 18 out of 72 F1 points in XLNet on HotpotQA come from true multi-fact reasoning, and introduces a transformation that reduces disconnected reasoning by 19 F1 points.
Has there been real progress in multi-hop question-answering? Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts. This limits our ability to measure true progress and defeats the purpose of building multi-hop QA datasets. We make three contributions towards addressing this. First, we formalize such undesirable behavior as disconnected reasoning across subsets of supporting facts. This allows developing a model-agnostic probe for measuring how much any model can cheat via disconnected reasoning. Second, using a notion of \emph{contrastive support sufficiency}, we introduce an automatic transformation of existing datasets that reduces the amount of disconnected reasoning. Third, our experiments suggest that there hasn't been much progress in multi-hop QA in the reading comprehension setting. For a recent large-scale model (XLNet), we show that only 18 points out of its answer F1 score of 72 on HotpotQA are obtained through multifact reasoning, roughly the same as that of a simpler RNN baseline. Our transformation substantially reduces disconnected reasoning (19 points in answer F1). It is complementary to adversarial approaches, yielding further reductions in conjunction.