WebCrawl African is a mixed domain multilingual parallel corpora for a pool of African languages compiled by ANVITA machine translation team of Centre for Artificial Intelligence and Robotics Lab, primarily for accelerating research on low-resource and extremely low-resource machine translation and is part of the submission to WMT 2022 shared task on Large-Scale Machine Translation Evaluation for African Languages under the data track. The corpora is compiled through web data mining and comprises 695K parallel sentences spanning 74 different language pairs from English and 15 African languages, many of which fall under low and extremely low resource categories. As a measure of corpora usefulness, a MNMT model for 24 African languages to English is trained by combining WebCrawl African corpora with existing corpus and evaluation on FLORES200 shows that inclusion of WebCrawl African corpora could improve BLEU score by 0.01-1.66 for 12 out of 15 African to English translation directions and even by 0.18-0.68 for the 4 out of 9 African to English translation directions which are not part of WebCrawl African corpora. WebCrawl African corpora includes more parallel sentences for many language pairs in comparison to OPUS public repository. This data description paper captures creation of corpora and results obtained along with datasheets. The WebCrawl African corpora is hosted on github repository.

This paper describes ANVITA African NMT system submitted by team ANVITA for WMT 2022 shared task on Large-Scale Machine Translation Evaluation for African Languages under the constrained translation track. The team participated in 24 African languages to English MT directions. For better handling of relatively low resource language pairs and effective transfer learning, models are trained in multilingual setting. Heuristic based corpus filtering is applied and it improved performance by 0.04-2.06 BLEU across 22 out of 24 African to English directions and also improved training time by 5x. Use of deep transformer with 24 layers of encoder and 6 layers of decoder significantly improved performance by 1.1-7.7 BLEU across all the 24 African to English directions compared to base transformer. For effective selection of source vocabulary in multilingual setting, joint and language wise vocabulary selection strategies are explored at the source side. Use of language wise vocabulary selection however did not consistently improve performance of low resource languages in comparison to joint vocabulary selection. Empirical results indicate that training using deep transformer with filtered corpora seems to be a better choice than using base transformer on the whole corpora both in terms of accuracy and training time.