File size: 6,726 Bytes
5792bfb
 
 
 
 
d8a13cd
5792bfb
d8a13cd
5f9512d
5792bfb
 
 
 
 
 
 
f8b41a0
27b0bf0
ef375a2
47fa199
27b0bf0
 
6a3c3d4
 
 
 
 
 
 
 
 
 
 
 
 
f7c625f
 
 
6a3c3d4
 
5792bfb
 
4fd862c
5792bfb
 
 
 
ef375a2
5792bfb
 
 
ef375a2
 
5792bfb
 
 
 
 
 
 
 
 
 
 
 
 
bd9eea4
5792bfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f8b41a0
5792bfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4fd862c
 
 
5792bfb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bd9eea4
 
 
27b0bf0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: e2e
pretty_name: End-to-End NLG Challenge
tags:
- meaning-representation-to-text
dataset_info:
  features:
  - name: meaning_representation
    dtype: string
  - name: human_reference
    dtype: string
  splits:
  - name: train
    num_bytes: 9435824
    num_examples: 42061
  - name: validation
    num_bytes: 1171723
    num_examples: 4672
  - name: test
    num_bytes: 1320205
    num_examples: 4693
  download_size: 11812316
  dataset_size: 11927752
---

# Dataset Card for End-to-End NLG Challenge

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [homepage](http://www.macs.hw.ac.uk/InteractionLab/E2E/)
- **Repository:** [repository](https://github.com/tuetschek/e2e-dataset/)
- **Paper:** [paper](https://arxiv.org/abs/1706.09254)
- **Leaderboard:** [leaderboard](http://www.macs.hw.ac.uk/InteractionLab/E2E/)


### Dataset Summary

The E2E dataset is used for training end-to-end, data-driven natural language generation systems in the restaurant domain, which is ten times bigger than existing, frequently used datasets in this area.
The E2E dataset poses new challenges:
(1) its human reference texts show more lexical richness and syntactic variation, including discourse phenomena;
(2) generating from this set requires content selection. As such, learning from this dataset promises more natural, varied and less template-like system utterances.

E2E is released in the following paper where you can find more details and baseline results:
https://arxiv.org/abs/1706.09254

### Supported Tasks and Leaderboards

- `text2text-generation-other-meaning-representation-to-text`: The dataset can be used to train a model to generate descriptions in the restaurant domain from meaning representations, which consists in taking as input some data about a restaurant and generate a sentence in natural language that presents the different aspects of the data about the restaurant.. Success on this task is typically measured by achieving a *high* [BLEU](https://huggingface.co/metrics/bleu), [NIST](https://huggingface.co/metrics/nist), [METEOR](https://huggingface.co/metrics/meteor), [Rouge-L](https://huggingface.co/metrics/rouge), [CIDEr](https://huggingface.co/metrics/cider). The TGen model (Dusek and Jurcıcek, 2016a) was used a baseline, had the following scores:

|          | BLEU	  | NIST   | METEOR | ROUGE_L | CIDEr  |
| -------- | ------ | ------ | ------ | ------- | ------ |
| BASELINE | 0.6593 |	8.6094 | 0.4483 | 0.6850  | 2.2338 |


This task has an inactive leaderboard which can be found [here](http://www.macs.hw.ac.uk/InteractionLab/E2E/) and ranks models based on the metrics above.

### Languages

The dataset is in english (en).

## Dataset Structure

### Data Instances

Example of one instance:

```
{'human_reference': 'The Vaults pub near Café Adriatic has a 5 star rating.  Prices start at £30.',
 'meaning_representation': 'name[The Vaults], eatType[pub], priceRange[more than £30], customer rating[5 out of 5], near[Café Adriatic]'}
```


### Data Fields

- `human_reference`: string, the text is natural language that describes the different characteristics in the meaning representation
- `meaning_representation`: list of slots and values to generate a description from

Each MR consists of 3–8 attributes (slots), such as name, food or area, and their values.

### Data Splits

The dataset is split into training, validation and testing sets (in a 76.5-8.5-15 ratio), keeping a similar distribution of MR and reference text lengths and ensuring that MRs in different sets are distinct.

|                            |  train |  validation |  test |
| -----                      |-------:|------------:|------:|
| N. Instances               |  42061 |        4672 |  4693 |

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

[More Information Needed]

#### Initial Data Collection and Normalization

The data was collected using the CrowdFlower platform and quality-controlled following Novikova et al. (2016).

#### Who are the source language producers?

[More Information Needed]

### Annotations

Following Novikova et al. (2016), the E2E data was collected using pictures as stimuli, which was shown to elicit significantly more natural, more informative, and better phrased human references than textual MRs.

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

```
@article{dusek.etal2020:csl,
  title = {Evaluating the {{State}}-of-the-{{Art}} of {{End}}-to-{{End Natural Language Generation}}: {{The E2E NLG Challenge}}},
  author = {Du{\v{s}}ek, Ond\v{r}ej and Novikova, Jekaterina and Rieser, Verena},
  year = {2020},
  month = jan,
  volume = {59},
  pages = {123--156},
  doi = {10.1016/j.csl.2019.06.009},
  archivePrefix = {arXiv},
  eprint = {1901.11528},
  eprinttype = {arxiv},
  journal = {Computer Speech \& Language}
```

### Contributions

Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset.