@InProceedings{wang-EtAl:2018:C18-11,
  author    = {Wang, Liang  and  Li, Sujian  and  Zhao, Wei  and  Shen, Kewei  and  Sun, Meng  and  Jia, Ruoyu  and  Liu, Jingming},
  title     = {Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {857--867},
  abstract  = {Cloze-style reading comprehension has been a popular task for measuring the progress of natural language understanding in recent years. In this paper, we design a novel multi-perspective framework, which can be seen as the joint training of heterogeneous experts and aggregate context information from different perspectives. Each perspective is modeled by a simple aggregation module. The outputs of multiple aggregation modules are fed into a one-timestep pointer network to get the final answer. At the same time, to tackle the problem of insufficient labeled data, we propose an efficient sampling mechanism to automatically generate more training examples by matching the distribution of candidates between labeled and unlabeled data. We conduct our experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017), which consists of nearly 100k questions designed by professional teachers. Results show that our method achieves new state-of-the-art performance over previous strong baselines.},
  url       = {http://www.aclweb.org/anthology/C18-1073}
}