Name
Abstract | ||
|---|---|---|
Most of question-answer pairs in question answering task are generated manually, which is inefficient and expensive. However, the existing work on automatic question generation is not good enough to replace manual annotation. This paper presents a system to generate questions from a knowledge base in Chinese. The contribution of our work contains two parts. First we offer a neural generation approach using long short term memory (LSTM). Second, we design a new format of input sequence for the system, which promotes the performance of the model. On the evaluation of KBQG of NLPCC 2018 Shared Task 7, our system achieved 73.73 BLEU, and took the first place in the evaluation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-99495-6_12 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Question answering,Generation,Knowledge base | Question answering,Computer science,Manual annotation,Long short term memory,Natural language processing,Artificial intelligence,Automatic question generation,Knowledge base,Question generation | Conference |
Volume | ISSN | Citations |
11108 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Wang | 1 | 1 | 1.02 |
| Xiaodong Zhang | 2 | 88 | 4.51 |
| Hou-Feng Wang | 3 | 611 | 53.83 |