chinese/question_answering.md
Question answering is the task of answering a question.
The Chinese Machine Reading Comprehension (CMRC 2018) is a SQuAD-like reading comprehension dataset that consists of 20,000 questions annotated on Wikipedia paragraphs by human experts. The dataset can be downloaded here. Below we show the F1 and EM scores both on the test set and the challenge set.
| Model | Test F1 | Test EM | Challenge F1 | Challenge EM | Paper |
|---|---|---|---|---|---|
| Human performance | 97.9 | 92.4 | 95.2 | 90.4 | A Span-Extraction Dataset for Chinese Machine Reading Comprehension |
| Dual BERT (w / SQuAD; Cui et al., 2019) | 90.2 | 73.6 | 55.2 | 27.8 | Cross-Lingual Machine Reading Comprehension |
| Dual BERT (Cui et al., 2019) | 88.1 | 70.4 | 47.9 | 23.8 | Cross-Lingual Machine Reading Comprehension |
The Delta Reading Comprehension Dataset (DRCD) is a SQuAD-like reading comprehension dataset that contains 30,000+ questions on 10,014 paragraphs from 2,108 Wikipedia articles. The dataset can be downloaded here.
| Model | F1 | EM | Paper |
|---|---|---|---|
| Human performance | 93.3 | 80.4 | DRCD: a Chinese Machine Reading Comprehension Dataset |
| Dual BERT (w / SQuAD; Cui et al., 2019) | 91.6 | 85.4 | Cross-Lingual Machine Reading Comprehension |
| Dual BERT (Cui et al., 2019) | 90.3 | 83.7 | Cross-Lingual Machine Reading Comprehension |
DuReader is a large-scale reading comprehension dataset that is based on the logs of Baidu Search and contains 200k questions, 420k answers, and 1M documents. For more information, refer to its website to see the introduction. You can download the dataset here. The best models can be view on the public leaderboard.