Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Adebara, Ife"'
We address a notable gap in Natural Language Processing (NLP) by introducing a collection of resources designed to improve Machine Translation (MT) for low-resource languages, with a specific focus on African languages. First, we introduce two langua
Externí odkaz:
http://arxiv.org/abs/2407.04796
We investigate two research questions: (1) how do machine translation (MT) and diacritization influence the performance of each other in a multi-task learning setting (2) the effect of keeping (vs. removing) diacritics on MT performance. We examine t
Externí odkaz:
http://arxiv.org/abs/2404.05943
Low-resource African languages pose unique challenges for natural language processing (NLP) tasks, including natural language generation (NLG). In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages. Chee
Externí odkaz:
http://arxiv.org/abs/2401.01053
ChatGPT has recently emerged as a powerful NLP tool that can carry out a variety of tasks. However, the range of languages ChatGPT can handle remains largely a mystery. To uncover which languages ChatGPT `knows', we investigate its language identific
Externí odkaz:
http://arxiv.org/abs/2311.09696
We describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where we tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual models under a full supervised setting (sub
Externí odkaz:
http://arxiv.org/abs/2304.11256
Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. To date, only ~31 out of ~2,000 African languages are covere
Externí odkaz:
http://arxiv.org/abs/2212.10785
Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world's 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing Afr
Externí odkaz:
http://arxiv.org/abs/2210.11744
Autor:
Adebara, Ife, Abdul-Mageed, Muhammad
Aligning with ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages", we discuss the major linguistic and sociopolitical challenges facing development of NLP technologies for African languages. Situating African lan
Externí odkaz:
http://arxiv.org/abs/2203.08351
Autor:
Adebara, Ife, Abdul-Mageed, Muhammad
We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we su
Externí odkaz:
http://arxiv.org/abs/2108.03533
Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morpho
Externí odkaz:
http://arxiv.org/abs/2103.04225