Name
Abstract | ||
|---|---|---|
Owing to the continuous contributions by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available, and they have been pushing Chinese MRC research forward. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We hope the release of the dataset could further accelerate the machine reading comprehension research. Resources available: https://github.com/ymcui/cmrc2019 |
Year | Venue | DocType |
|---|---|---|
2020 | COLING | Conference |
Volume | Citations | PageRank |
2020.coling-main | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiming Cui | 1 | 87 | 13.40 |
| Ting Liu | 2 | 2735 | 232.31 |
| Yang Ziqing | 3 | 0 | 0.68 |
| zhipeng chen | 4 | 83 | 10.24 |
| Wentao Ma | 5 | 117 | 20.19 |
| Wanxiang Che | 6 | 711 | 66.39 |
| Shijin Wang | 7 | 180 | 31.56 |
| Guoping Hu | 8 | 309 | 37.32 |