- Description:
Czech data-to-text dataset in the restaurant domain. The input meaning representations contain a dialogue act type (inform, confirm etc.), slots (food, area, etc.) and their values. It originated as a translation of the English San Francisco Restaurants dataset by Wen et al. (2015).
Source code:
tfds.structured.cs_restaurants.CSRestaurants
Versions:
1.0.0
(default): No release notes.
Download size:
1.40 MiB
Dataset size:
2.46 MiB
Auto-cached (documentation): Yes
Splits:
Split | Examples |
---|---|
'test' |
842 |
'train' |
3,569 |
'validation' |
781 |
- Feature structure:
FeaturesDict({
'delex_input_text': FeaturesDict({
'table': Sequence({
'column_header': string,
'content': string,
'row_number': int16,
}),
}),
'delex_target_text': string,
'input_text': FeaturesDict({
'table': Sequence({
'column_header': string,
'content': string,
'row_number': int16,
}),
}),
'target_text': string,
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
delex_input_text | FeaturesDict | |||
delex_input_text/table | Sequence | |||
delex_input_text/table/column_header | Tensor | string | ||
delex_input_text/table/content | Tensor | string | ||
delex_input_text/table/row_number | Tensor | int16 | ||
delex_target_text | Tensor | string | ||
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_neural_2019,
author = {Dušek, Ondřej and Jurčíček, Filip},
title = {Neural {Generation} for {Czech}: {Data} and {Baselines} },
shorttitle = {Neural {Generation} for {Czech} },
url = {https://www.aclweb.org/anthology/W19-8670/},
urldate = {2019-10-18},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
month = oct,
address = {Tokyo, Japan},
year = {2019},
pages = {563--574},
abstract = {We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires inflecting named entities, delexicalization or copy mechanisms do not work out-of-the-box and lexicalizing the generated outputs is non-trivial. In our experiments, we present two different approaches to this this problem: (1) using a neural language model to select the correct inflected form while lexicalizing, (2) a two-step generation setup: our sequence-to-sequence model generates an interleaved sequence of lemmas and morphological tags, which are then inflected by a morphological generator.},
}