Natural Language Processing in E thiopian Languages: Current State, Challenges, and Opportunities (2023)

Abstract: 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 repository 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.

## introduction due to the rise of its applications in many fields, natural language processing (nlp), a sub-field of artificial intelligence (ai), is receiving a lot of attention in terms of research and development @xcite . nlp tasks such as machine translation (mt), sentiment or opinion analysis, parts of speech (pos) tagging, question classification (qc) and answering (qa), chunking, named entity recognition (ner), emotion detection, and semantic role labeling is currently highly researched areas in different high-resource languages. because of the advancement of deep learning and transformer approaches, modern nlp systems rely largely on the availability of vast volumes of annotated and unannotated data to function well. the majority of the languages in the world do not have access to such enormous information tools, despite the fact that a few high-resource languages have received more attention. ethiopia is a country with more than 85 spoken languages, but only a few are presented in nlp progress. figure 1 shows a search result for articles found in the acl anthology for high and low-resource languages. as we can see from figure 1 , the search result for low-resource languages shows a very insignificant number of research works, while high-resource languages like english dominate in the acl anthology paper repository. this might be a reflection of the unavailability of resources in the digital world, like in other high-resource languages, which affected the nlp progress in low-resource languages in general and ethiopian languages in particular. in this paper, we overview research works done in the area of selected nlp tasks for four ethiopian languages. we cover mainly the following 4 languages, namely amharic (amh), afaan oromo (orm), tigrinya (tir), and wolaytta (wal). we also reviewed works on a small set of local languages including awigna (awn) and kistane(gru), specially for the machine translation tasks. the contributions of this paper are as follows: ## language details this paper assesses the progress of nlp research for four ethiopian languages: amharic, afaan oromo, tigrinya, and wolaytta. as ethiopia is a multilingual, multicultural, and multi-ethnic country, those selected languages have more speakers and native speakers in the country. additionally, we have searched papers in the major eight ethiopian languages and taken the top four based on frequency from the acl anthology. this section gives some descriptions of those four targeted languages. amharic: is an ethio-semitic and afro-asiatic language. it is the official working language of the federal democratic republic of ethiopia (fdre). it has about 57 million speakers, which makes it the second most spoken semitic language in the world, where 32 million of them are native speakers @xcite . other known names for this language are amarigna and amarinya. afaan oromo: is a cushitic language family. the language name may be written in different alternatives: (afan, afaan, affan) oromo, or simply oromo. there are over 50 million native oromo speakers @xcite . tigrinya: (alternatively: tigregna, tigrinya or tigrigna) is a semitic language family spoken in the tigray region of ethiopia and in eritrea. the language uses ge'ez script with some additional alphabets that have been created for the tigrinya language and are closely related to ge'ez and amharic. the language has around 9.9 million native speakers @xcite . wolaytta: (alternatively: wolayita, wolaytegna, wolaytigna, welaitta, and welayita) is an omotic language family spoken in the wolaytta zone of ethiopia. both afan oromo and wolaytta are written in the latin alphabet. ## low-resource languages researchers concerned with nlp have used data availability (either in the form of labeled, unlabeled, or auxiliary data) and nlp tools and resources as criteria for defining low-resource languages @xcite . according to the work by @xcite , low-resource languages lack the tools and resources important for nlp and other techno-linguistic solutions. in addition, low-resource languages lack new language technology designs. due to all these limitations, it is very difficult to develop new powerful methods for language applications @xcite . for resource-rich languages such as english, germany, french, spanish and etc, the size of the dataset is not a problem because researchers have created a large set of corpora and tools for many nlp tasks. however, many other languages are deemed to be low-resource languages @xcite . with this intuition, ethiopian languages such as amharic @xcite , afaan oromo (abate et al., 2019), tigrinya (osman and mikami, 2012), wolayitta @xcite are "low-resource" languages due to lack of data resources, linguistic materials, and tools. this affected the development of different nlp tasks and tools. ## possible resource sources and tools data is one of the crucial building blocks for any nlp application. the availability of data is one of the criteria to categorize one language as a high or low-resource language @xcite . as discussed in section 3 ethiopian languages belong to low-resource languages due to the unavailability of data. like data, nlp tools are also one of the building blocks for nlp applications, and the unavailability of these tools for a certain language also directly affects the development of nlp applications for that language. ## nlp tasks and their progress in this section, we discuss what work has been done, what datasets of what sizes were used, what methods or approaches the authors proposed, and the availability of their dataset and models for nlp tasks and their progress in selected ethiopian languages. we focused on machine translation (mt), part-of-speech (pos) tagging, named entity recognition (ner), question classification (qc), question answering (qa), text classification, and text summarization tasks due to the large number of works done for the targeted low-resource languages. the available models, the datasets for the tasks, and their links are found in ## conclusion in this work, we investigated the most common nlp tasks and research works carried out in four ethiopian languages. we explored the main nlp research directions, progress, challenges, and opportunities for ethiopian languages. our findings revealed that a significant amount of research has been centered on english or amharic-centric machine translation tasks. despite there being a plethora of written languages in ethiopia, only a few of them have been explored in common research studies. additionally, we observed a low prevalence of valuable resource publications in international conference venues. the majority of works are master's theses. the publicly available datasets, models, and tools are released in a centralized github repository 2 . in the future, we plan to conduct a survey on more african languages and try to come up with an nlp resource demarcation line that could help funders to prioritize research topics and languages. figure 7 shows the dataset availability per publication year. it can be noted from the table that in recent works there are attempts to make datasets available for ethiopian languages but this still needs more effort.