# Multi-Instance Learning for End-to-End Knowledge Base Question Answering Source: [Multi-Instance Learning for End-to-End Knowledge Base Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:27:45.299Z Authors: Mengxi Wei, Yifan He, Qiong Zhang, Luo Si Published: 2019-03-06T23:14:49.000Z ## Abstract End-to-end training has been a popular approach for knowledge base question answering (KBQA). However, real world applications often contain answers of varied quality for users' questions. It is not appropriate to treat all available answers of a user question equally. This paper proposes a novel approach based on multiple instance learning to address the problem of noisy answers by exploring consensus among answers to the same question in training end-to-end KBQA models. In particular, the QA pairs are organized into bags with dynamic instance selection and different options of instance weighting. Curriculum learning is utilized to select instance bags during training. On the public CQA dataset, the new method significantly improves both entity accuracy and the Rouge-L score over a state-of-the-art end-to-end KBQA baseline. ## 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]()