Name
Abstract | ||
|---|---|---|
Question answering is an important problem that aims to deliver specific answers to questions posed by humans in natural language. How to efficiently identify the exact answer with respect to a given question has become an active line of research. Previous approaches in factoid question answering tasks typically focus on modeling the semantic relevance or syntactic relationship between a given question and its corresponding answer. Most of these models suffer when a question contains very little content that is indicative of the answer. In this paper, we devise an architecture named the temporality-enhanced knowledge memory network (TE-KMN) and apply the model to a factoid question answering dataset from a trivia competition called quiz bowl. Unlike most of the existing approaches, our model encodes not only the content of questions and answers, but also the temporal cues in a sequence of ordered sentences which gradually remark the answer. Moreover, our model collaboratively uses external knowledge for a better understanding of a given question. The experimental results demonstrate that our method achieves better performance than several state-of-the-art methods. |
Year | Venue | Keywords |
|---|---|---|
2018 | Frontiers of IT & EE | Question answering, Knowledge memory, Temporality interaction, TP391 |
Field | DocType | Volume |
Architecture,Mathematical optimization,Question answering,Computer science,Semantic relevance,Natural language,Artificial intelligence,Natural language processing,Factoid,Syntax,Temporality | Journal | 19 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
5 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinyu Duan | 1 | 25 | 3.15 |
| Siliang Tang | 2 | 179 | 33.98 |
| Sheng-yu Zhang | 3 | 1 | 1.36 |
| Yin Zhang | 4 | 3492 | 281.04 |
| Zhou Zhao | 5 | 773 | 90.87 |
| J. Xue | 6 | 542 | 57.57 |
| Yue-Ting Zhuang | 7 | 3549 | 216.06 |
| Fei Wu | 8 | 2209 | 153.88 |