Update dataset card

Update dataset card:
- Add license
- Add repository URL
- Update citation information

README.md

@@ -14,8 +14,7 @@ language:
 - qu
 - shp
 - tar
-license:
-- unknown
+license: cc-by-sa-4.0
 multilinguality:
 - multilingual
 - translation
@@ -370,6 +369,7 @@ configs:
 ## Dataset Description
 
 - **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/abteen/americasnli
 - **Repository:** https://github.com/nala-cub/AmericasNLI
 - **Paper:** https://arxiv.org/abs/2104.08726
 - **Leaderboard:** [Needs More Information]
@@ -613,40 +613,38 @@ As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
 
 ### Licensing Information
 
-[Needs More Information]
+Creative Commons Attribution Share Alike 4.0 International: https://github.com/abteen/americasnli/blob/main/LICENSE.md
 
 ### Citation Information
 
 ```
-@article{DBLP:journals/corr/abs-2104-08726,
-  author    = {Abteen Ebrahimi and
-               Manuel Mager and
-               Arturo Oncevay and
-               Vishrav Chaudhary and
-               Luis Chiruzzo and
-               Angela Fan and
-               John Ortega and
-               Ricardo Ramos and
-               Annette Rios and
-               Ivan Vladimir and
-               Gustavo A. Gim{\'{e}}nez{-}Lugo and
-               Elisabeth Mager and
-               Graham Neubig and
-               Alexis Palmer and
-               Rolando A. Coto Solano and
-               Ngoc Thang Vu and
-               Katharina Kann},
-  title     = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
-               Pretrained Multilingual Models in Truly Low-resource Languages},
-  journal   = {CoRR},
-  volume    = {abs/2104.08726},
-  year      = {2021},
-  url       = {https://arxiv.org/abs/2104.08726},
-  eprinttype = {arXiv},
-  eprint    = {2104.08726},
-  timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
-  biburl    = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
-  bibsource = {dblp computer science bibliography, https://dblp.org}
+@inproceedings{ebrahimi-etal-2022-americasnli,
+    title = "{A}mericas{NLI}: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages",
+    author = "Ebrahimi, Abteen  and
+      Mager, Manuel  and
+      Oncevay, Arturo  and
+      Chaudhary, Vishrav  and
+      Chiruzzo, Luis  and
+      Fan, Angela  and
+      Ortega, John  and
+      Ramos, Ricardo  and
+      Rios, Annette  and
+      Meza Ruiz, Ivan Vladimir  and
+      Gim{\'e}nez-Lugo, Gustavo  and
+      Mager, Elisabeth  and
+      Neubig, Graham  and
+      Palmer, Alexis  and
+      Coto-Solano, Rolando  and
+      Vu, Thang  and
+      Kann, Katharina",
+    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = may,
+    year = "2022",
+    address = "Dublin, Ireland",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.acl-long.435",
+    pages = "6279--6299",
+    abstract = "Pretrained multilingual models are able to perform cross-lingual transfer in a zero-shot setting, even for languages unseen during pretraining. However, prior work evaluating performance on unseen languages has largely been limited to low-level, syntactic tasks, and it remains unclear if zero-shot learning of high-level, semantic tasks is possible for unseen languages. To explore this question, we present AmericasNLI, an extension of XNLI (Conneau et al., 2018) to 10 Indigenous languages of the Americas. We conduct experiments with XLM-R, testing multiple zero-shot and translation-based approaches. Additionally, we explore model adaptation via continued pretraining and provide an analysis of the dataset by considering hypothesis-only models. We find that XLM-R{'}s zero-shot performance is poor for all 10 languages, with an average performance of 38.48{\%}. Continued pretraining offers improvements, with an average accuracy of 43.85{\%}. Surprisingly, training on poorly translated data by far outperforms all other methods with an accuracy of 49.12{\%}.",
 }
 ```