Update README.md

README.md

@@ -1,3 +1,64 @@
 ---
 license: afl-3.0
 ---
+
+### Citation Information
+```
+@inproceedings{adelani-etal-2022-thousand,
+    title = "A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for {A}frican News Translation",
+    author = "Adelani, David  and
+      Alabi, Jesujoba  and
+      Fan, Angela  and
+      Kreutzer, Julia  and
+      Shen, Xiaoyu  and
+      Reid, Machel  and
+      Ruiter, Dana  and
+      Klakow, Dietrich  and
+      Nabende, Peter  and
+      Chang, Ernie  and
+      Gwadabe, Tajuddeen  and
+      Sackey, Freshia  and
+      Dossou, Bonaventure F. P.  and
+      Emezue, Chris  and
+      Leong, Colin  and
+      Beukman, Michael  and
+      Muhammad, Shamsuddeen  and
+      Jarso, Guyo  and
+      Yousuf, Oreen  and
+      Niyongabo Rubungo, Andre  and
+      Hacheme, Gilles  and
+      Wairagala, Eric Peter  and
+      Nasir, Muhammad Umair  and
+      Ajibade, Benjamin  and
+      Ajayi, Tunde  and
+      Gitau, Yvonne  and
+      Abbott, Jade  and
+      Ahmed, Mohamed  and
+      Ochieng, Millicent  and
+      Aremu, Anuoluwapo  and
+      Ogayo, Perez  and
+      Mukiibi, Jonathan  and
+      Ouoba Kabore, Fatoumata  and
+      Kalipe, Godson  and
+      Mbaye, Derguene  and
+      Tapo, Allahsera Auguste  and
+      Memdjokam Koagne, Victoire  and
+      Munkoh-Buabeng, Edwin  and
+      Wagner, Valencia  and
+      Abdulmumin, Idris  and
+      Awokoya, Ayodele  and
+      Buzaaba, Happy  and
+      Sibanda, Blessing  and
+      Bukula, Andiswa  and
+      Manthalu, Sam",
+    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = jul,
+    year = "2022",
+    address = "Seattle, United States",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.naacl-main.223",
+    doi = "10.18653/v1/2022.naacl-main.223",
+    pages = "3053--3070",
+    abstract = "Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.",
+}
+```