- Description:
An update release of E2E NLG Challenge data with cleaned MRs. The E2E data contains dialogue act-based meaning representation (MR) in the restaurant domain and up to 5 references in natural language, which is what one needs to predict.
Additional Documentation: Explore on Papers With Code
Source code:
tfds.datasets.e2e_cleaned.Builder
Versions:
0.1.0
(default): No release notes.
Download size:
13.92 MiB
Dataset size:
14.70 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
4,693 |
'train' |
33,525 |
'validation' |
4,299 |
- Feature structure:
FeaturesDict({
'input_text': FeaturesDict({
'table': Sequence({
'column_header': string,
'content': string,
'row_number': int16,
}),
}),
'target_text': string,
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
input_text | FeaturesDict | |||
input_text/table | Sequence | |||
input_text/table/column_header | Tensor | string | ||
input_text/table/content | Tensor | string | ||
input_text/table/row_number | Tensor | int16 | ||
target_text | Tensor | string |
Supervised keys (See
as_supervised
doc):('input_text', 'target_text')
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
@inproceedings{dusek-etal-2019-semantic,
title = "Semantic Noise Matters for Neural Natural Language Generation",
author = "Du{\v{s} }ek, Ond{\v{r} }ej and
Howcroft, David M. and
Rieser, Verena",
booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
month = oct # "{--}" # nov,
year = "2019",
address = "Tokyo, Japan",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W19-8652",
doi = "10.18653/v1/W19-8652",
pages = "421--426",
abstract = "Neural natural language generation (NNLG) systems are known for their pathological outputs, i.e. generating text which is unrelated to the input specification. In this paper, we show the impact of semantic noise on state-of-the-art NNLG models which implement different semantic control mechanisms. We find that cleaned data can improve semantic correctness by up to 97{\%}, while maintaining fluency. We also find that the most common error is omitting information, rather than hallucination.",
}