| layout | permalink |
|---|---|
sparc |
sparc |
SParC is a dataset for cross-domain Semantic Parsing in Context. It is the context-dependent/multi-turn version of the Spider task, a complex and cross-domain text-to-SQL challenge. SParC consists of 4,298 coherent question sequences (12k+ unique individual questions annotated with SQL queries annotated by 14 Yale students), obtained from user interactions with 200 complex databases over 138 domains.
XLang Lab for building LM agents!
SParC Paper (ACL'19)
SParC Post
Related works: DS-1000, Binder, UnifiedSKG, single-turn Spider and conversational CoSQL text-to-SQL tasks. DS-1000 Challenge ('22) Binder Framework (ICLR '23) UnifiedSKG Framework (EMNLP '22) Spider Chanllenge (EMNLP'18) CoSQL Chanllenge (EMNLP'19)
- 08/10/2023 Please check out XLang language model agents!
- 11/20/2022 Please check out our recent work DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. Please check out examples, data, and code on the DS-1000 project site!!
- 10/18/2022 Please check out our recent work Binder: an easy but sota neural-symbolic built on GPT-3 Codex & SQL/Python interpreter. It injects GPT-3 Codex prompt API calls in programming languages! Please check out Binder demo, code, paper, and video on the Binder project site!!
- 02/15/2022 Please check out our recent work UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. We open-sourced simple but SOTA/strong models for 21 tasks including text-to-SQL! Please check out our code in the UnifiedSKG repo!!
- 11/15/2020 We will use Test Suite Accuracy as our official evaluation metric for Spider, SParC, and CoSQL. Please find the evaluation code from here.
- 5/17/2019 Our paper SParC: Cross-Domain Semantic Parsing in Context with Salesforce Research was accepted to ACL 2019!
SParC is built upon the Spider dataset. Comparing to other existing context-dependent semantic parsing/text-to-SQL datasets such as ATIS, it demonstrates:
- complex contextual dependencies (annotated by 15 Yale computer science students)
- has greater semantic diversity due to complex coverage of SQL logic patterns in the Spider dataset.
- requires generalization to new domains due to its cross-domain nature and the unseen databasest time.
The data is split into training, development, and unreleased test sets. Download a copy of the dataset (distributed under the CC BY-SA 4.0 license):
SParC Dataset
Details of baseline models and evaluation script can be found on the following GitHub site:
SParC GitHub Page
Once you have built a model that works to your expectations on the dev set,
you can submit it to get official scores on the dev and a hidden test set. To preserve the
integrity of test results, we do not release the test set to the public. Instead, we request
you to submit your model so that we can run it on the test set for you. Here's a tutorial walking you through official evaluation of your model:
Submission Tutorial
Some examples look like the following:
Another example:
We expect the dataset to evolve. We would greatly appreciate it if you could donate us your non-private databases or SQL queries for the project.
We thank Tianze Shi and the anonymous reviewers for their precious comments on this project and Melvin Gruesbeck for designing the nice example illustrations. Also, we thank Pranav Rajpurkar for giving us the permission to build this website based on SQuAD. .
Part of our SParC team at YINS:
Tweet
<script>!function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0],p=/^http:/.test(d.location)?'http':'https';if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src=p+'://platform.twitter.com/widgets.js';fjs.parentNode.insertBefore(js,fjs);}}(document, 'script', 'twitter-wjs');</script>
Our current models do not predict any value in SQL conditions so that we do not provide execution accuracies. However, we encourage you to provide it in the future submissions. For value prediction, your model should be able to 1) copy from the question inputs, 2) retrieve from the database content (database content is available), or 3) generate numbers (e.g. 3 in "LIMIT 3").
| Rank | Model | Question Match | Interaction Match |
|---|---|---|---|
|
1 Jun 4, 2022 |
RASAT + PICARD
SJTU LUMIA & Netmind.AI (Qi et al., EMNLP'22) code |
74.0 | 52.6 |
|
2 May 24, 2020 |
TreeSQL V2 + BERT
Anonymous |
48.5 | 21.6 |
|
3 May 21, 2020 |
GAZP + BERT
University of Washington & Facebook AI Research (Zhong et al., EMNLP '20) |
44.6 | 19.7 |
For exact matching evaluation, instead of simply conducting string comparison between the predicted and gold SQL queries, we decompose each SQL into several clauses, and conduct set comparison in each SQL clause. Please refer to the paper and the Github page for more details.
| Rank | Model | Question Match | Interaction Match |
|---|---|---|---|
|
1 Feb 14, 2022 |
STAR
Alibaba DAMO & SIAT (Cai and Li et al., EMNLP-Findings '22) code demo |
67.4 | 46.6 |
|
2 Jun 4, 2022 |
RASAT + PICARD
SJTU LUMIA & Netmind.AI (Qi et al., EMNLP'22) code |
67.7 | 45.2 |
|
3 Apr 27, 2022 |
CQR-SQL
Tencent Cloud Xiaowei (Xiao et al.,'22) |
68.2 | 44.4 |
|
4 Oct 8, 2021 |
RAT-SQL-TC + GAP
Meituan & PKU (Li et al.,'21) |
65.7 | 43.2 |
|
5 Oct 18, 2021 |
HIE-SQL + GraPPa
Alibaba DAMO (Zheng et al. ACL-Findings '22) |
64.6 | 42.9 |
|
6 Sep. 21, 2020 |
RAT-SQL + SCoRe
Yale & Microsoft Research & PSU (Yu et al. ICLR '21) |
62.4 | 38.1 |
|
7 Oct 21, 2020 |
WaveSQL+BERT
Anonymous |
58.7 | 33.3 |
|
8 July 08, 2020 |
R²SQL + BERT
Alibaba DAMO (Hui et al. AAAI '21) code |
55.8 | 30.8 |
|
9 May 26, 2020 |
IGSQL + BERT
Peking University (Cai et al. EMNLP '20) code |
51.2 | 29.5 |
|
10 Jun. 02, 2020 |
MIE + BERT
Anonymous |
49.6 | 27.1 |
|
11 May 04, 2020 |
SubTreeSQL + BERT
Anonymous |
47.4 | 25.5 |
|
12 Sep 1, 2019 |
EditSQL + BERT
Yale University & Salesforce Research (Zhang et al. EMNLP '19) code |
47.9 | 25.3 |
|
13 May 03, 2020 |
TreeSQL V2 + BERT
Anonymous |
48.1 | 25.0 |
|
14 May 22, 2020 |
MH-LTA + BERT
Anonymous |
48.5 | 24.7 |
|
15 Jan 15, 2020 |
TreeSQL + BERT
Anonymous |
46.3 | 24.3 |
|
16 May 21, 2020 |
GAZP + BERT
University of Washington & Facebook AI Research (Zhong et al., EMNLP '20) |
45.9 | 23.5 |
|
17 Feb 13, 2020 |
ConcatSQL + BERT
Anonymous |
46.3 | 22.4 |
|
18 Apr 21, 2021 |
MemCE
UoE (Jain et al., TACL '21) |
40.3 | 16.7 |
|
19 Feb 13, 2020 |
ConcatSQL
Anonymous |
39.0 | 16.3 |
|
20 Dec 13, 2019 |
GuideSQL
Anonymous |
34.4 | 13.1 |
|
21 May 17, 2019 |
CD-Seq2Seq
Yale University & Salesforce Research (Yu et al. ACL '19) code |
23.2 | 7.5 |
|
22 May 17, 2019 |
SyntaxSQL-con
Yale University (Yu et al. EMNLP '18) code |
20.2 | 5.2 |