EthioNLP/Ethiopian-Language-Survey

Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities # Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities - arxiv This survey delves into the current state of natural language processing (NLP) for four Ethiopian languages: Amharic, Afaan Oromo, Tigrinya, and Wolaytta. Through this paper, we identify key challenges and opportunities for NLP research in Ethiopia. Furthermore, we provide a centralized repository on GitHub that contains publicly available resources for various NLP tasks in these languages. This reposi tory can be updated periodically with contributions from other researchers. Our objective is to disseminate information to NLP researchers interested in Ethiopian languages and encourage future research in this domain. ## 1. NLP Tools |Tools Name|Tools task |Language support|Resource link| |----------|-----------|----------------|-------------| |amseg | Segmenter, tokenizer, transliteration, romanization and normalization| Amharic | amseg | |HornMorpho| Morphological analysis| Amhric, Afaan Ormo, Tigirgna |HornMorpho | |lemma | Lemmatizer | Amhric |lemma| ## 2. NLP Applications ### 2.1. Machine Translation (MT) We discuss the MT progress for Ethiopian languages in three categories: **English Centeric** -> works done for the above target Ethiopian languages with English pair, **Ethiopian - Ethiopian** -> works done for Ethiopian language pairs without involving other languages and **Multilingual MT** -> works done for Ethiopian languages with other languages in a multilingual setting. #### 2.1.1 English centeric - Parallel Corpora Preparation for English-Amharic Machine Translation - Extended Parallel Corpus for Amharic-English Machine Translation - Context based machine translation with recurrent neural network for English–Amharic translation - Offline Corpus Augmentation for English-Amharic Machine Translation - The Effect of Normalization for Bi-directional Amharic-English Neural Machine Translation - Optimal A …