Name
Abstract | ||
|---|---|---|
Sentence-level question answering (QA) for news articles is a promising task for social media, whose task is to make machine understand a news article and answer a corresponding question with an answer sentence selected from the news article. Recently, several deep neural networks have been proposed for sentence-level QA. For the best of our knowledge, none of them explicitly use keywords that appear simultaneously in questions and documents. In this paper we introduce the Attention-based Memory Network (Att-MemNN), a new iterative bi-directional attention memory network that predicts answer sentences. It exploits the co-occurrence of keywords among questions and documents as augment inputs of deep neural network and embeds documents and corresponding questions in different way, processing questions with word-level and contextual-level embedding while processing documents only with word-level embedding. Experimental results on the test set of NewsQA show that our model yields great improvement. We also use quantitative and qualitative analysis to show the results intuitively. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-981-10-6805-8_9 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Sentence-level question answering for news articles,Attention mechanism,Memory network,Deep learning | Conference | 774 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei Liu | 1 | 4 | 4.47 |
| Chunhong Zhang | 2 | 93 | 20.35 |
| Weiming Zhang | 3 | 83 | 15.80 |
| Zhiqiang Zhan | 4 | 8 | 2.12 |
| Benhui Zhuang | 5 | 0 | 0.34 |