# Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering Source: [Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:16:13.371Z Authors: Hao Cheng, Ming-Wei Chang, Kenton Lee, Kristina Toutanova Published: 2020-05-05T01:08:36.000Z ## Abstract We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings. We compare previously used probability space and distant super-vision assumptions (assumptions on the correspondence between the weak answer string labels and possible answer mention spans). We show that these assumptions interact, and that different configurations provide complementary benefits. We demonstrate that a multi-objective model can efficiently combine the advantages of multiple assumptions and out-perform the best individual formulation. Our approach outperforms previous state-of-the-art models by 4.3 points in F1 on TriviaQA-Wiki and 1.7 points in Rouge-L on NarrativeQA summaries. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()