# Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering Source: [Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:24:19.140Z Authors: Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, Wei Xu Published: 2016-07-21T11:40:50.000Z ## Abstract While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset WebQA with more than 42k questions and 556k evidences. As existing neural QA methods resolve QA either as sequence generation or classification/ranking problem, they face challenges of expensive softmax computation, unseen answers handling or separate candidate answer generation component. In this work, we cast neural QA as a sequence labeling problem and propose an end-to-end sequence labeling model, which overcomes all the above challenges. Experimental results on WebQA show that our model outperforms the baselines significantly with an F1 score of 74.69% with word-based input, and the performance drops only 3.72 F1 points with more challenging character-based input. ## 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]()