Name
Title | ||
|---|---|---|
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering. |
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. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computation and Language | F1 score,Sequence labeling,Question answering,Softmax function,Ranking,Computer science,Natural language processing,Artificial intelligence,Artificial neural network,Factoid,Machine learning,Computation |
DocType | Volume | Citations |
Journal | abs/1607.06275 | 13 |
PageRank | References | Authors |
1.01 | 25 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Li | 1 | 146 | 21.34 |
| Wei Li | 2 | 396 | 76.68 |
| Zhengyan He | 3 | 77 | 4.95 |
| Xuguang Wang | 4 | 52 | 4.18 |
| Ying Cao | 5 | 57 | 9.01 |
| jie zhou | 6 | 184 | 9.01 |
| Wei Xu | 7 | 13 | 4.73 |