Update metadata (#5)

- Update metadata (97917f3b68883df0963ffba2db53c51898f34638)

README.md

@@ -6,7 +6,7 @@ language_creators:
 language:
 - en
 license:
-- cc-by-4.0
+- cc-by-sa-4.0
 multilinguality:
 - monolingual
 size_categories:
@@ -76,11 +76,14 @@ dataset_info:
 
 ## Dataset Description
 
-- **Homepage:** [SNLI homepage](https://nlp.stanford.edu/projects/snli/)
-- **Repository:**
-- **Paper:** [A large annotated corpus for learning natural langauge inference](https://nlp.stanford.edu/pubs/snli_paper.pdf)
-- **Leaderboard:** [SNLI leaderboard](https://nlp.stanford.edu/projects/snli/) (located on the homepage)
-- **Point of Contact:** [Samuel Bowman](mailto:bowman@nyu.edu) and [Gabor Angeli](mailto:angeli@stanford.edu)
+- **Homepage:** https://nlp.stanford.edu/projects/snli/
+- **Repository:** [More Information Needed]
+- **Paper:** https://aclanthology.org/D15-1075/
+- **Paper:** https://arxiv.org/abs/1508.05326
+- **Leaderboard:** https://nlp.stanford.edu/projects/snli/
+- **Point of Contact:** [Samuel Bowman](mailto:bowman@nyu.edu)
+- **Point of Contact:** [Gabor Angeli](mailto:angeli@stanford.edu)
+- **Point of Contact:** [Chris Manning](manning@stanford.edu)
 
 ### Dataset Summary
 
@@ -88,7 +91,9 @@ The SNLI corpus (version 1.0) is a collection of 570k human-written English sent
 
 ### Supported Tasks and Leaderboards
 
-[SemBERT](https://arxiv.org/pdf/1909.02209.pdf) (Zhousheng Zhang et al, 2019b) is currently listed as SOTA, achieving 91.9% accuracy on the test set. See the [corpus webpage](https://nlp.stanford.edu/projects/snli/) for a list of published results.
+Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is the task of determining the inference relation between two (short, ordered) texts: entailment, contradiction, or neutral ([MacCartney and Manning 2008](https://aclanthology.org/C08-1066/)).
+
+See the [corpus webpage](https://nlp.stanford.edu/projects/snli/) for a list of published results.
 
 ### Languages
 
@@ -144,7 +149,7 @@ The hypotheses were elicited by presenting crowdworkers with captions from preex
 
 Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).
 
-The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://www.aclweb.org/anthology/Q14-1006.pdf), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/).
+The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://aclanthology.org/Q14-1006/), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/).
 
 The premises from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted.
 
@@ -154,7 +159,7 @@ A large portion of the premises (160k) were produced in the [Flickr 30k corpus](
 
 The Flickr 30k corpus did not report crowdworker or photo subject demographic information or crowdworker compensation. The SNLI crowdworkers were compensated per HIT at rates between $.1 and $.5 with no incentives. Workers who ignored the guidelines were disqualified, and automated bulk submissions were rejected. No demographic information was collected from the SNLI crowdworkers.
 
-An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://visualgenome.org/static/paper/Visual_Genome.pdf). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.
+An additional 4,000 premises come from the pilot study of the [VisualGenome corpus](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html). Though the pilot study itself is not described, the location information of the 33,000 AMT crowdworkers that participated over the course of the 6 months of data collection are aggregated. Most of the workers were located in the United States (93%), with others from the Philippines, Kenya, India, Russia, and Canada. Workers were paid $6-$8 per hour.
 
 ### Annotations
 
@@ -187,11 +192,11 @@ This dataset was developed as a benchmark for evaluating representational system
 
 ### Discussion of Biases
 
-The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://www.aclweb.org/anthology/W17-1609.pdf) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.
+The language reflects the content of the photos collected from Flickr, as described in the [Data Collection](#initial-data-collection-and-normalization) section. [Rudinger et al (2017)](https://aclanthology.org/W17-1609/) use pointwise mutual information to calculate a measure of association between a manually selected list of tokens corresponding to identity categories and the other words in the corpus, showing strong evidence of stereotypes across gender categories. They also provide examples in which crowdworkers reproduced harmful stereotypes or pejorative language in the hypotheses.
 
 ### Other Known Limitations
 
-[Gururangan et al (2018)](https://www.aclweb.org/anthology/N18-2017.pdf), [Poliak et al (2018)](https://www.aclweb.org/anthology/S18-2023.pdf), and [Tsuchiya (2018)](https://www.aclweb.org/anthology/L18-1239.pdf) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time.
+[Gururangan et al (2018)](https://aclanthology.org/N18-2017/), [Poliak et al (2018)](https://aclanthology.org/S18-2023/), and [Tsuchiya (2018)](https://aclanthology.org/L18-1239/) show that the SNLI corpus has a number of annotation artifacts. Using various classifiers, Poliak et al correctly predicted the label of the hypothesis 69% of the time without using the premise, Gururangan et al 67% of the time, and Tsuchiya 63% of the time.
 
 ## Additional Information
 
@@ -203,20 +208,60 @@ It was supported by a Google Faculty Research Award, a gift from Bloomberg L.P.,
 
 ### Licensing Information
 
-The Stanford Natural Language Inference Corpus is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
+The Stanford Natural Language Inference Corpus by The Stanford NLP Group is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).
+
+The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), also released under an Attribution-ShareAlike licence.
 
 ### Citation Information
 
+The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it: 
+```bibtex
+@inproceedings{bowman-etal-2015-large,
+    title = "A large annotated corpus for learning natural language inference",
+    author = "Bowman, Samuel R.  and
+      Angeli, Gabor  and
+      Potts, Christopher  and
+      Manning, Christopher D.",
+    editor = "M{\`a}rquez, Llu{\'\i}s  and
+      Callison-Burch, Chris  and
+      Su, Jian",
+    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
+    month = sep,
+    year = "2015",
+    address = "Lisbon, Portugal",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D15-1075",
+    doi = "10.18653/v1/D15-1075",
+    pages = "632--642",
+}
 ```
-@inproceedings{snli:emnlp2015,
-	Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
-	Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-	Publisher = {Association for Computational Linguistics},
-	Title = {A large annotated corpus for learning natural language inference},
-	Year = {2015}
+
+The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/), which can be cited by way of this paper: 
+```bibtex
+@article{young-etal-2014-image,
+    title = "From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions",
+    author = "Young, Peter  and
+      Lai, Alice  and
+      Hodosh, Micah  and
+      Hockenmaier, Julia",
+    editor = "Lin, Dekang  and
+      Collins, Michael  and
+      Lee, Lillian",
+    journal = "Transactions of the Association for Computational Linguistics",
+    volume = "2",
+    year = "2014",
+    address = "Cambridge, MA",
+    publisher = "MIT Press",
+    url = "https://aclanthology.org/Q14-1006",
+    doi = "10.1162/tacl_a_00166",
+    pages = "67--78",
 }
 ```
 
+### Contact Information
+
+For any comments or questions, please email [Samuel Bowman](mailto:bowman@nyu.edu), [Gabor Angeli](mailto:angeli@stanford.edu) and [Chris Manning](manning@stanford.edu). 
+
 ### Contributions
 
 Thanks to [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.

snli.py

@@ -24,12 +24,23 @@ import datasets
 
 
 _CITATION = """\
-@inproceedings{snli:emnlp2015,
-    Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
-    Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
-    Publisher = {Association for Computational Linguistics},
-    Title = {A large annotated corpus for learning natural language inference},
-    Year = {2015}
+@inproceedings{bowman-etal-2015-large,
+    title = "A large annotated corpus for learning natural language inference",
+    author = "Bowman, Samuel R.  and
+      Angeli, Gabor  and
+      Potts, Christopher  and
+      Manning, Christopher D.",
+    editor = "M{\\`a}rquez, Llu{\\'\\i}s  and
+      Callison-Burch, Chris  and
+      Su, Jian",
+    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
+    month = sep,
+    year = "2015",
+    address = "Lisbon, Portugal",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D15-1075",
+    doi = "10.18653/v1/D15-1075",
+    pages = "632--642",
 }
 """