Datasets:
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids:
- unknown
pretty_name: dart
tags:
- data-to-text
Dataset Card for GEM/dart
Dataset Description
- Homepage: n/a
- Repository: https://github.com/Yale-LILY/dart
- Paper: https://aclanthology.org/2021.naacl-main.37/
- Leaderboard: https://github.com/Yale-LILY/dart#leaderboard
- Point of Contact: Dragomir Radev, Rui Zhang, Nazneen Rajani
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
DART is an English dataset aggregating multiple other data-to-text dataset in a common triple-based format. The new format is completely flat, thus not requiring a model to learn hierarchical structures, while still retaining the full information.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/dart')
The data loader can be found here.
website
n/a
paper
authors
Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Dataset Overview
Where to find the Data and its Documentation
Download
Paper
BibTex
@inproceedings{nan-etal-2021-dart,
title = "{DART}: Open-Domain Structured Data Record to Text Generation",
author = "Nan, Linyong and
Radev, Dragomir and
Zhang, Rui and
Rau, Amrit and
Sivaprasad, Abhinand and
Hsieh, Chiachun and
Tang, Xiangru and
Vyas, Aadit and
Verma, Neha and
Krishna, Pranav and
Liu, Yangxiaokang and
Irwanto, Nadia and
Pan, Jessica and
Rahman, Faiaz and
Zaidi, Ahmad and
Mutuma, Mutethia and
Tarabar, Yasin and
Gupta, Ankit and
Yu, Tao and
Tan, Yi Chern and
Lin, Xi Victoria and
Xiong, Caiming and
Socher, Richard and
Rajani, Nazneen Fatema",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.37",
doi = "10.18653/v1/2021.naacl-main.37",
pages = "432--447",
abstract = "We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.",
}
Contact Name
Dragomir Radev, Rui Zhang, Nazneen Rajani
Contact Email
{dragomir.radev, r.zhang}@yale.edu, {nazneen.rajani}@salesforce.com
Has a Leaderboard?
yes
Leaderboard Link
Leaderboard Details
Several state-of-the-art table-to-text models were evaluated on DART, such as BART (Lewis et al., 2020), Seq2Seq-Att (MELBOURNE) and End-to-End Transformer (Castro Ferreira et al., 2019). The leaderboard reports BLEU, METEOR, TER, MoverScore, BERTScore and BLEURT scores.
Languages and Intended Use
Multilingual?
no
Covered Dialects
It is an aggregated from multiple other datasets that use general US-American or British English without differentiation between dialects.
Covered Languages
English
Whose Language?
The dataset is aggregated from multiple others that were crowdsourced on different platforms.
License
mit: MIT License
Intended Use
The dataset is aimed to further research in natural language generation from semantic data.
Primary Task
Data-to-Text
Communicative Goal
The speaker is required to produce coherent sentences and construct a trees structured ontology of the column headers.
Credit
Curation Organization Type(s)
academic, industry
Curation Organization(s)
Yale University, Salesforce Research, Penn State University, The University of Hong Kong, MIT
Dataset Creators
Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
Who added the Dataset to GEM?
Miruna Clinciu contributed the original data card and Yacine Jernite wrote the initial data loader. Sebastian Gehrmann migrated the data card and the loader to the new format.
Dataset Structure
Data Fields
-tripleset: a list of tuples, each tuple has 3 items
-subtree_was_extended: a boolean variable (true or false)
-annotations: a list of dict, each with source and text keys.
-source: a string mentioning the name of the source table.
-text: a sentence string.
Reason for Structure
The structure is supposed to be able more complex structures beyond "flat" attribute-value pairs, instead encoding hierarchical relationships.
How were labels chosen?
They are a combination of those from existing datasets and new annotations that take advantage of the hierarchical structure
Example Instance
{
"tripleset": [
[
"Ben Mauk",
"High school",
"Kenton"
],
[
"Ben Mauk",
"College",
"Wake Forest Cincinnati"
]
],
"subtree_was_extended": false,
"annotations": [
{
"source": "WikiTableQuestions_lily",
"text": "Ben Mauk, who attended Kenton High School, attended Wake Forest Cincinnati for college."
}
]
}
Data Splits
|Input Unit | Examples | Vocab Size | Words per SR | Sents per SR | Tables | | ------------- | ------------- || ------------- || ------------- || ------------- || ------------- | |Triple Set | 82,191 | 33.2K | 21.6 | 1.5 | 5,623 |
| Train | Dev | Test| | ------------- | ------------- || ------------- | | 62,659 | 6,980 | 12,552|
Statistics of DART decomposed by different collection methods. DART exhibits a great deal of topical variety in terms of the number of unique predicates, the number of unique triples, and the vocabulary size. These statistics are computed from DART v1.1.1; the number of unique predicates reported is post-unification (see Section 3.4). SR: Surface Realization. (details in Table 1 and 2).
Splitting Criteria
For WebNLG 2017 and Cleaned E2E, DART use the original data splits. For the new annotation on WikiTableQuestions and WikiSQL, random splitting will make train, dev, and test splits contain similar tables and similar <triple-set, sentence> examples. They are thus split based on Jaccard similarity such that no training examples has a similarity with a test example of over 0.5
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.
Similar Datasets
yes
Unique Language Coverage
no
Difference from other GEM datasets
The tree structure is unique among GEM datasets
Ability that the Dataset measures
Reasoning, surface realization
GEM-Specific Curation
Modificatied for GEM?
no
Additional Splits?
no
Getting Started with the Task
Pointers to Resources
Experimental results on DART shows that BART model as the highest performance among three models with a BLEU score of 37.06. This is attributed to BART’s generalization ability due to pretraining (Table 4).
Previous Results
Previous Results
Measured Model Abilities
Reasoning, surface realization
Metrics
BLEU, MoverScore, BERT-Score, BLEURT
Proposed Evaluation
The leaderboard uses the combination of BLEU, METEOR, TER, MoverScore, BERTScore, PARENT and BLEURT to overcome the limitations of the n-gram overlap metrics.
A small scale human annotation of 100 data points was conducted along the dimensions of (1) fluency - a sentence is natural and grammatical, and (2) semantic faithfulness - a sentence is supported by the input triples.
Previous results available?
yes
Other Evaluation Approaches
n/a
Relevant Previous Results
BART currently achieves the best performance according to the leaderboard.
Dataset Curation
Original Curation
Original Curation Rationale
The dataset creators encourage through DART further research in natural language generation from semantic data. DART provides high-quality sentence annotations with each input being a set of entity-relation triples in a tree structure.
Communicative Goal
The speaker is required to produce coherent sentences and construct a trees structured ontology of the column headers.
Sourced from Different Sources
yes
Source Details
- human annotation on open-domain Wikipedia tables from WikiTableQuestions (Pasupat and Liang, 2015) and WikiSQL (Zhong et al., 2017)
- automatic conversion of questions in WikiSQL to declarative sentences
- incorporation of existing datasets including WebNLG 2017 (Gardent et al., 2017a,b; Shimorina and Gardent, 2018) and Cleaned E2E (Novikova et al., 2017b; Dušek et al., 2018, 2019)
Language Data
How was Language Data Obtained?
Found, Created for the dataset
Where was it found?
Offline media collection
Creation Process
Creators proposed a two-stage annotation process for constructing triple set sentence pairs based on a tree-structured ontology of each table. First, internal skilled annotators denote the parent column for each column header. Then, a larger number of annotators provide a sentential description of an automatically-chosen subset of table cells in a row. To form a triple set sentence pair, the highlighted cells can be converted to a connected triple set automatically according to the column ontology for the given table.
Language Producers
No further information about the MTurk workers has been provided.
Topics Covered
The sub-datasets are from Wikipedia, DBPedia, and artificially created restaurant data.
Data Validation
validated by crowdworker
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
none
Annotation Service?
no
Consent
Any Consent Policy?
no
Justification for Using the Data
The new annotations are based on Wikipedia which is in the public domain and the other two datasets permit reuse (with attribution)
Private Identifying Information (PII)
Contains PII?
no PII
Justification for no PII
None of the datasets talk about individuals
Maintenance
Any Maintenance Plan?
no
Broader Social Context
Previous Work on the Social Impact of the Dataset
Usage of Models based on the Data
no
Impact on Under-Served Communities
Addresses needs of underserved Communities?
no
Discussion of Biases
Any Documented Social Biases?
no
Are the Language Producers Representative of the Language?
No, the annotators are raters on crowdworking platforms and thus only represent their demographics.
Considerations for Using the Data
PII Risks and Liability
Licenses
Copyright Restrictions on the Dataset
open license - commercial use allowed
Copyright Restrictions on the Language Data
open license - commercial use allowed
Known Technical Limitations
Technical Limitations
The dataset may contain some social biases, as the input sentences are based on Wikipedia (WikiTableQuestions, WikiSQL, WebNLG). Studies have shown that the English Wikipedia contains gender biases(Dinan et al., 2020), racial biases([Papakyriakopoulos et al., 2020 (https://dl.acm.org/doi/pdf/10.1145/3351095.3372843)) and geographical bias(Livingstone et al., 2010). More info.
Unsuited Applications
The end-to-end transformer has the lowest performance since the transformer model needs intermediate pipeline planning steps to have higher performance. Similar findings can be found in Castro Ferreira et al., 2019.