参考：
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:disfl_qa')
- 说明：
Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting,
namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018)
dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as
a source of distractors.
The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90% of the disfluencies are
corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a
major gap between speech and NLP research community. We hope the dataset can serve as a benchmark dataset for
testing robustness of models against disfluent inputs.
Our expriments reveal that the state-of-the-art models are brittle when subjected to disfluent inputs from
Disfl-QA. Detailed experiments and analyses can be found in our paper.
- 许可：Disfl-QA 数据集在 CC BY 4.0 下许可
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
3643 |
'train' |
7182 |
'validation' |
1000 |
- 特征：
{
"squad_v2_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"original question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"disfluent question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}