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Question Answering for complex questions is often modeled as a graphconstruction or traversal task, where a solver must build or traverse a graphof facts that answer and explain a given question. This "multi-hop" inferencehas been shown to be extremely challenging, with few models able to aggregatemore than two facts before being overwhelmed by "semantic drift", or thetendency for long chains of facts to quickly drift off topic. This is a majorbarrier to current inference models, as even elementary science questionsrequire an average of 4 to 6 facts to answer and explain. In this work weempirically characterize the difficulty of building or traversing a graph ofsentences connected by lexical overlap, by evaluating chance sentenceaggregation quality through 9,784 manually-annotated judgments across knowledgegraphs built from three free-text corpora (including study guides and SimpleWikipedia). We demonstrate semantic drift tends to be high and aggregationquality low, at between 0.04% and 3%, and highlight scenarios that maximize thelikelihood of meaningfully combining information.

Multi-hop Inference for Sentence-level TextGraphs: How Challenging is Meaningfully Combining Information for Science Question Answering?