- **Description**:
Question generation using squad dataset using data splits described in 'Neural
Question Generation from Text: A Preliminary Study' (Zhou et al, 2017) and
'Learning to Ask: Neural Question Generation for Reading Comprehension' (Du et
al, 2017).
- **Homepage** :
[https://github.com/xinyadu/nqg @inproceedings{du-etal-2017-learning, title = "Learning to Ask: Neural Question Generation for Reading Comprehension", author = "Du, Xinya and Shao, Junru and Cardie, Claire", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", month = jul, year = "2017", address = "Vancouver, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/P17-1123", doi = "10.18653/v1/P17-1123", pages = "1342--1352", }](https://github.com/xinyadu/nqg%20@inproceedings%7Bdu-etal-2017-learning,%20title%20=%20%22Learning%20to%20Ask:%20Neural%20Question%20Generation%20for%20Reading%20Comprehension%22,%20author%20=%20%22Du,%20Xinya%20and%20Shao,%20Junru%20and%20Cardie,%20Claire%22,%20booktitle%20=%20%22Proceedings%20of%20the%2055th%20Annual%20Meeting%20of%20the%20Association%20for%20Computational%20Linguistics%20(Volume%201:%20Long%20Papers)",
month = jul, year = "2017", address = "Vancouver, Canada", publisher =
"Association for Computational Linguistics", url =
"https://aclanthology.org/P17-1123", doi = "10.18653/v1/P17-1123", pages =
"1342--1352", } )
- **Source code** :
[`tfds.text.squad_question_generation.SquadQuestionGeneration`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/text/squad_question_generation/squad_question_generation.py)
- **Versions**:
- `1.0.0`: Initial build with unique SQuAD QAS ids in each split, using
passage-level context (Zhou et al, 2017).
- `2.0.0`: Matches the original split of (Zhou et al, 2017), allows both
sentence- and passage-level contexts, and uses answers from (Zhou et al,
2017).
- **`3.0.0`** (default): Added the split of (Du et al, 2017) also.
- **Auto-cached**
([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
Yes
- **Supervised keys** (See
[`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
`('context_passage', 'question')`
- **Figure**
([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):
Not supported.
- **Citation**:
@inproceedings{du-etal-2017-learning,
title = "Learning to Ask: Neural Question Generation for Reading Comprehension",
author = "Du, Xinya and Shao, Junru and Cardie, Claire",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1123",
doi = "10.18653/v1/P17-1123",
pages = "1342--1352",
}
@inproceedings{rajpurkar-etal-2016-squad,
title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
author = "Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D16-1264",
doi = "10.18653/v1/D16-1264",
pages = "2383--2392",
}
## squad_question_generation/split_du (default config)
- **Config description**: Answer independent question generation from
passage-level contexts (Du et al, 2017).
- **Download size** : `62.83 MiB`
- **Dataset size** : `84.67 MiB`
- **Splits**:
| Split | Examples |
|---|---|
| `'test'` | 11,877 |
| `'train'` | 75,722 |
| `'validation'` | 10,570 |
- **Feature structure**:
FeaturesDict({
'answer': Text(shape=(), dtype=string),
'context_passage': Text(shape=(), dtype=string),
'question': Text(shape=(), dtype=string),
})
- **Feature documentation**:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| | FeaturesDict | | | |
| answer | Text | | string | |
| context_passage | Text | | string | |
| question | Text | | string | |
- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):
## squad_question_generation/split_zhou
- **Config description**: Answer-span dependent question generation from
sentence- and passage-level contexts (Zhou et al, 2017).
- **Download size** : `62.52 MiB`
- **Dataset size** : `111.02 MiB`
- **Splits**:
| Split | Examples |
|---|---|
| `'test'` | 8,964 |
| `'train'` | 86,635 |
| `'validation'` | 8,965 |
- **Feature structure**:
FeaturesDict({
'answer': Text(shape=(), dtype=string),
'context_passage': Text(shape=(), dtype=string),
'context_sentence': Text(shape=(), dtype=string),
'question': Text(shape=(), dtype=string),
})
- **Feature documentation**:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| | FeaturesDict | | | |
| answer | Text | | string | |
| context_passage | Text | | string | |
| context_sentence | Text | | string | |
| question | Text | | string | |
- **Examples** ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):