Dureader is an end-to-end neural networks model for machine reading comprehesion style question answering, which aims to anser questions from given passages. We first match the question and passage with a bidireactional attention flow networks to obtrain the question-aware passages represenation. Then we employ the pointer networks to locate the positions of answers from passages. Our experimental evalutions show that DuReader model achieves the state-of-the-art results in DuReader Dadaset.
Dureader is an end-to-end neural network model for machine reading comprehesion style question answering, which aims to anser questions from given passages. We first match the question and passage with a bidireactional attention flow networks to obtrain the question-aware passages represenation. Then we employ the pointer networks to locate the positions of answers from passages. Our experimental evalutions show that DuReader model achieves the state-of-the-art results in DuReader Dadaset.
# Dataset
DuReader Dataset is a new large-scale real-world and human sourced MRC dataset in Chinese. DuReader focuses on real-world open-domain question answering. The advantages of DuReader over existing datasets are concluded as follows:
- Real question
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# Network
DuReader is inspired by 3 classic reading comprehension models([BiDAF](https://arxiv.org/abs/1611.01603), [Match-LSTM](https://arxiv.org/abs/1608.07905), [R-NET](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf)).
DuReader model is a hierarchical multi_stage process adn consist of five layers
DuReader model is a hierarchical multi-stage process and consists of five layers
-**Word Embedding Layer** maps each word to a vector space using a pre-trained word embedding model.
-**Encoding Layer** extract context infomation for each position in question and passages with bi-directional LSTM network.
-**Word Embedding Layer** maps each word to a vector using a pre-trained word embedding model.
-**Encoding Layer** extracts context infomation for each position in question and passages with bi-directional LSTM network.
-**Attention Flow Layer** couples the query and context vectors and produces a set of query-aware feature vectors for each word in the context. Please refer to [BiDAF](https://arxiv.org/abs/1611.01603) for more details.
-**Fusion Layer** employs two layers of bi-directional LSTM to capture the interaction among context words independent of the query.
-**Answer Point Network Layer with Attention Pooling** please refer to [Match-LSTM](https://arxiv.org/abs/1608.07905) and [R_NET](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf) for more details.
-**Decode Layer** employs an answer point network with attention pooling of the quesiton to locate the positions of answers from passages. Please refer to [Match-LSTM](https://arxiv.org/abs/1608.07905) and [R-NET](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf) for more details.
## How to Run
### Download the Dataset
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For more details about DuReader dataset please refer to [DuReader Dataset Homepage](https://ai.baidu.com//broad/subordinate?dataset=dureader).
### Download Thirdparty Dependencies
We use Bleu and Rouge as evaluation metrics, the calculation of these metrics relies on the scoring scripts under "https://github.com/tylin/coco-caption", to download them, run:
We use Bleu and Rouge as evaluation metrics, the calculation of these metrics relies on the scoring scripts under [coco-caption](https://github.com/tylin/coco-caption), to download them, run:
