@InProceedings{yu-EtAl:2018:W18-26,
  author    = {Yu, Seunghak  and  Indurthi, Sathish Reddy  and  Back, Seohyun  and  Lee, Haejun},
  title     = {A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension},
  booktitle = {Proceedings of the Workshop on Machine Reading for Question Answering},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {21--30},
  abstract  = {Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.},
  url       = {http://www.aclweb.org/anthology/W18-2603}
}