Name
Abstract | ||
|---|---|---|
When people do the reading comprehension, they often try to find the words from the passages which are similar to the question words first. Then people deduce the answer based on the context around these similar words. Therefore, the position information may be helpful in finding the answer rapidly and is useful for reading comprehension. However, previous attention-based machine reading comprehension models typically focus on the interaction between the question and the context representation without considering the position information. In this paper, we introduce the position information to machine reading comprehension and investigate the performance of the position information. The position information is experimented in three different ways: 1) position encoder; 2) attention mechanism; and 3) position mapping embedding. By experimenting on TriviaQA dataset, we have demonstrated the effectiveness of position information. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2930407 | IEEE ACCESS |
Keywords | DocType | Volume |
Task analysis, Context modeling, Knowledge discovery, Natural languages, Predictive models, Kernel, Computational modeling, Attention mechanism, machine comprehension, position information | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yajing Xu | 1 | 11 | 5.28 |
| Weijie Liu | 2 | 0 | 0.34 |
| Guang Chen | 3 | 0 | 0.34 |
| Boya Ren | 4 | 0 | 0.34 |
| Siman Zhang | 5 | 0 | 0.34 |
| Sheng Gao | 6 | 444 | 47.81 |
| Jun Guo | 7 | 0 | 0.34 |