# Question Answering through Transfer Learning from Large Fine-grained Supervision Data Source: [Question Answering through Transfer Learning from Large Fine-grained Supervision Data]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:05:03.439Z Authors: Sewon Min, Minjoon Seo, Hannaneh Hajishirzi Published: 2017-02-07T19:22:06.000Z ## Abstract We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset. We achieve the state of the art in two well-studied QA datasets, WikiQA and SemEval-2016 (Task 3A), through a basic transfer learning technique from SQuAD. For WikiQA, our model outperforms the previous best model by more than 8%. We demonstrate that finer supervision provides better guidance for learning lexical and syntactic information than coarser supervision, through quantitative results and visual analysis. We also show that a similar transfer learning procedure achieves the state of the art on an entailment task. ## 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]()