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- 1. Exploring the Challenges of Natural Language Processing (NLP) in Ethiopian Language Submitted to: - Dr. Tibebe Beshahesta DECEMBER 27, 2023 HiLCoE School of Computer Science &Technology Group Members ID Number 1. Abel Hailemariam QF7953 2. Alazar Kebede FX4743 3. Anobie Tesfaye LD1791 4. Dagim Ashenafi XI9378 5. Enat Desta UZ9773 6. Kalkidan Abebe VJ9284 Information Research Method: - Research Proposal
- 2. 1 Table of Contents Background/Overview................................................................................................................................2 Problem Statement......................................................................................................................................3 Research Questions.................................................................................................................................3 Objective of the Research...........................................................................................................................4 a) General Objective...............................................................................................................................4 b) Specific Objectives..............................................................................................................................4 Approach/Methodology..............................................................................................................................4 General Approach...................................................................................................................................4 Study Population.....................................................................................................................................4 Data Collection Methods ........................................................................................................................4 Data Analysis...........................................................................................................................................5 Design/Experiment Methods..................................................................................................................5 Procedures/Tools and Techniques.........................................................................................................5 Literature Review .......................................................................................................................................5 Scope and limitations of the research........................................................................................................7 Scope ........................................................................................................................................................7 Limitations...............................................................................................................................................8 Significance of the research........................................................................................................................8 References....................................................................................................................................................9 Annex .........................................................................................................................................................10
- 3. 2 Background/Overview The field of Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to enable computers to understand, analyze, and generate human language. It has gained significant attention and success in several major languages and advancement in recent years, particularly in languages with extensive research and resources available. However, non-English languages, particularly those with a smaller digital presence and limited resources, often face considerable challenges when it comes to NLP. Ethiopian languages, with their rich linguistic diversity and unique characteristics, present a compelling case for investigating the challenges and possibilities in the realm of NLP. The general area that this research proposal focuses on is the exploration of NLP in Ethiopian languages. These languages play a vital role in Ethiopia's culture, society, and communication, yet their inclusion within the realm of NLP has been relatively limited. By addressing the challenges specific to Ethiopian languages, we aim to contribute to the broader field of NLP and foster linguistic diversity and inclusion. Key concepts include: ❖ Morphological Complexity: Ethiopian languages are known for their intricate morphology, involving complex word formations and extensive morphological processes. This presents challenges in developing effective morphological analyzers, segmentary, and stemmers, which are essential components of NLP systems. ❖ Limited Linguistic Resources: Ethiopian languages have relatively fewer linguistic resources available compared to more widely spoken languages. These include corpora, lexicons, annotated data, and language models. The scarcity of such resources poses difficulties in training and evaluating NLP models, hindering the progress of language- specific applications. ❖ Orthographic Variation: Ethiopian languages exhibit diverse orthographic conventions, with variations in script usage, character encoding, and writing systems. These variations impact text normalization, tokenization, and other preprocessing tasks crucial for NLP applications, requiring robust and adaptable techniques to handle them effectively. ❖ Named Entity Recognition (NER): NER is a fundamental task in NLP, and its accurate implementation in Ethiopian languages is an ongoing challenge. The lack of labeled datasets, ambiguous semantics, and the absence of standardized conventions hinder the development of robust NER models for these languages. ❖ Machine Translation and Language Generation: Enabling machine translation and language generation capabilities in Ethiopian languages would greatly facilitate communication, knowledge sharing, and information access. However, the scarcity of parallel corpora, translation models, and language models poses substantial obstacles in developing effective systems. In conclusion, this research proposal aims to dive into the challenges faced in applying NLP techniques to Ethiopian languages. By addressing the complexities of morphology, limited
- 4. 3 linguistic resources, orthographic variations, named entity recognition, machine translation, and language generation, we seek to provide insights and solutions that can empower the NLP community to tackle these challenges effectively. Through this exploration, we strive to promote the inclusion and advancement of Ethiopian languages in the broader field of NLP. Problem Statement The problem at hand is the limited progress and utilization of Ethiopian languages in NLP. The field of Natural Language Processing (NLP) has made significant strides in processing and understanding various languages. However, research and development in NLP have primarily focused on widely spoken languages, leaving Ethiopian languages largely understudied and neglected. This lack of attention creates a significant problem as it hampers the development of robust NLP applications for Ethiopian languages, hindering communication, access to information, and technological advancements within the Ethiopian context. The problem is twofold: Firstly, there is a scarcity of resources necessary for NLP in Ethiopian languages. This scarcity includes linguistic corpora, lexicons, annotated data, and language models, which are crucial for training and evaluating NLP systems. Without adequate resources, researchers and developers face significant challenges in building effective and accurate NLP models for these languages (Alemu et al., 2019). Additionally, the limited availability of parallel corpora and translation models inhibits progress in machine translation and language generation tasks tailored to Ethiopian languages (Tamirat and van Zaanen, 2017). Secondly, Ethiopian languages exhibit complex morphological structures, orthographic variations, and unconventional script usage, which complicate the application of existing NLP techniques (Worku et al., 2021). For instance, the intricate morphology of Ethiopian languages poses challenges in developing reliable morphological analyzers, segmentary, and stemmers (Beyene et al., 2013). Furthermore, orthographic variations in script usage and character encoding necessitate the need for adaptable preprocessing techniques to handle these complexities effectively (Abebe et al., 2020). Research Questions 1. What are the specific challenges of NLP in under-resourced Ethiopian languages, such as Amharic, Afaan Oromoo and Tigrinya? 2. How do the lexical and morphological complexities of Ethiopian languages impact NLP tasks, such as part-of-speech tagging, named entity recognition, and machine translation? 3. What are the implications of the lack of annotated data for developing robust NLP models in Ethiopian languages? 4. How can the dialectal and regional variations of Ethiopian languages be addressed to establish a standardized form for NLP applications?
- 5. 4 5. What are the potential solutions offered by cross-lingual transfer learning techniques to overcome the scarcity of resources in Ethiopian languages and improve NLP capabilities? Objective of the Research a) General Objective To address the challenges of NLP in Ethiopian languages and contribute towards the development of robust NLP models and resources, enabling effective communication, information retrieval, and technological advancements within the Ethiopian context. b) Specific Objectives • To investigate and identify the specific challenges faced in developing NLP applications for Ethiopian languages due to limited linguistic resources, such as corpora, lexicons, annotated data, and language models. • To propose and develop innovative techniques and methodologies for handling the complex morphological structures, orthographic variations, and unconventional script usage in Ethiopian languages, thereby enhancing the accuracy and performance of NLP models. • To explore and evaluate strategies for addressing the scarcity of parallel corpora and translation models, with a focus on developing machine translation and language generation systems tailored to Ethiopian languages. Approach/Methodology General Approach In this research proposal, a qualitative approach will be employed to explore the challenges of Natural Language Processing (NLP) in Ethiopian languages. The specific method chosen for this study is the Case Study method. Study Population The study population will consist of native speakers of Ethiopian languages and experts in the field of NLP. Native speakers will provide valuable insights into the unique linguistic characteristics, cultural nuances, and challenges of Ethiopian languages. NLP experts will provide technical expertise and guidance in identifying and addressing the challenges faced in developing NLP applications for these languages. Data Collection Methods 1. Literature Review: A comprehensive review of existing literature on NLP in Ethiopian languages will be conducted, analyzing previous studies, research papers, and relevant resources to gain an understanding of the current state and challenges in this field. 2. Interviews: Semi-structured interviews will be conducted with native speakers of Ethiopian languages who possess expertise in linguistics, computational linguistics, or NLP. These interviews will provide insights into the challenges, needs, and aspirations for NLP in Ethiopian languages.
- 6. 5 The sample size will be determined based on achieving data saturation, where new information or perspectives no longer emerge from additional participants. This saturation point will be used to limit the sample and ensure thorough coverage of the research topic while optimizing available resources. Please note, however, that the specific details of the saturation point, such as the number of participants, will be determined during the research process based on the evolving nature of the data. 3. Multilingual NLP Systems: Existing multilingual NLP systems, such as language models or named entity recognition tools, will be utilized to analyze the performance and limitations when processing Ethiopian languages. The outputs of these systems will be evaluated, and any errors or difficulties encountered will be documented. Data Analysis Thematic analysis will be used to analyze the qualitative data collected from interviews and observations. The data will be transcribed, coded, and categorized into themes and patterns. These themes will provide insights into the challenges and potential solutions for NLP in Ethiopian languages. Design/Experiment Methods The research design will involve a single or multiple case studies focusing on specific Ethiopian languages. The case studies will include the development and evaluation of prototypes and NLP systems for targeted language(s), incorporating the identified challenges and potential solutions. The performance metrics, such as accuracy, precision, recall, and linguistic coverage, will be used to evaluate the effectiveness of these systems. Procedures/Tools and Techniques 1. Purposive Sampling: Participants for interviews will be selected through purposive sampling, ensuring a diverse range of expertise and perspectives among native speakers of Ethiopian languages. 2. Transcription and Translation: Interviews will be audio-recorded, transcribed, and translated from the local language to English for analysis and interpretation purposes. 3. Qualitative Data Analysis Software: Specialized software, such as Atlas.ti, will be employed to facilitate the coding, organization, and analysis of qualitative data. 4. Ethical Considerations: All necessary ethical approvals and consent procedures will be followed to ensure the privacy and confidentiality of the participants. Informed consent will be obtained from all participants, and their identities will be anonymized in the research findings. Literature Review
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- 8. 7 Scope and limitations of the research Scope • Language Focus: The research will focus on three specific Ethiopian languages, namely Amharic, Afaan Oromoo, and Tigrinya. These languages are widely spoken in Ethiopia and represent a diverse linguistic landscape. By focusing on multiple languages, the research aims to capture a broader spectrum of challenges and explore language-specific nuances in NLP. • Multilingual NLP System: The research will employ a multilingual NLP system, specifically BERT (Bidirectional Encoder Representations from Transformers). BERT is a pre-trained language model that can handle multiple languages, including the ones
- 9. 8 chosen for this study. By utilizing BERT, the research aims to evaluate its effectiveness and adaptability in addressing the challenges of NLP in Ethiopian languages. Limitations However, it should be acknowledged that this research has certain limitations. Primarily, the chosen languages may not fully represent the linguistic diversity of Ethiopia, as there are numerous other languages within the country. The findings of this research may not be applicable to all Ethiopian languages due to this limitation. Additionally, the generalizability of the findings may be restricted to the selected languages and the specific research context. Furthermore, the research is constrained by time, resources, and the scope of coverage, which may limit the comprehensiveness of the study. The use of BERT as a multilingual NLP system carries its own limitations, as its effectiveness may vary across different languages. Lastly, qualitative research is subjective in nature and can be influenced by the researcher's interpretation and biases, despite efforts to ensure objectivity and rigor. Significance of the research The significance of research on natural language processing (NLP) for Ethiopian languages lies in its potential to address challenges related to digital inclusion and language preservation. Ethiopia's linguistic diversity, with over 80 distinct languages, necessitates the development of NLP technologies to meet the needs of the local population. By overcoming barriers to digital access, these technologies can empower individuals and bridge the digital divide, benefiting sectors like education, healthcare, governance, and commerce. Ethiopia is a linguistically diverse country, yet the development of NLP technologies has primarily focused on widely spoken languages, leaving speakers of Ethiopian languages with limited access to digital resources in their mother tongues. This research aims to fill this gap by exploring innovative approaches and techniques to develop NLP technologies specifically for Ethiopian languages. Understanding the unique linguistic characteristics of these languages allows for the design of algorithms, models, and tools tailored to their analysis and processing. This, in turn, enables the creation of applications such as machine translation, sentiment analysis, speech recognition, and information retrieval in Ethiopian languages. The significance of this research extends to several areas. Firstly, it promotes digital inclusion by ensuring individuals who primarily communicate in Ethiopian languages can fully participate in the digital era. Access to technology and digital content in one's native language empowers individuals to engage in online activities, access information, and communicate effectively, leading to enhanced social and economic opportunities. Secondly, the research contributes to language preservation and revitalization. By developing NLP technologies for Ethiopian languages, it aids in the documentation and archiving of these languages. Digital preservation of language resources safeguards Ethiopia's linguistic heritage and provides valuable resources for future generations to study, analyze, and revive endangered or under-represented languages.
- 10. 9 References • Demeke, G., & Getachew, M. (2006). Manual annotation of Amharic news items with part-of-speech tags and its challenges. Ethiopian Languages Research Center Working Papers, 2(1), 16. • Abebe, A., van Zaanen, M., & Bosch, A. (2020). The Role of Script in Under-resourced NLP: Empirical and Computational Approaches for Ethiopic Script. In Proceedings of the 1st International Workshop on Solutions for Automatic Gleaning of Multilingual Endangered Texts (SAGE) (pp. 1-10). • Alemu, H. H., Abate, S. S., & Worku, A. G. (2019). An initiative on Ethiopian languages localization: A case study approach. In Proceedings of the 4th ACM Workshop on African Network Information Center (pp. 10-16). • Beyene, A., Abebe, A., & Bosch, A. (2013). Challenges in Computational Analysis of Amharic Language Texts: Allomorph in Amharic Verb Inflection. In Proceedings of the 2013 AFNLP Conference (pp. 23-30). • Diab, M., Hacioglu, K., & Jurafsky, D. (2004). Automatic tagging of Arabic text: From raw text to base phrase chunks. In Proceedings of NAACL-HLT (pp. 149-152). • Gambäck, B., Olsson, F., Argaw, A. A., & Asker, L. (2009). Methods for Amharic part-of- speech tagging. In Proceedings of AfLaT. • Gasser, M. (2009). Horn Morpho: A system for morphological processing of Amharic, Oromo, and Tigrinya. In Proceedings of the 14th Meeting of Computational Linguistics in Africa (AfLaT). • Gasser, M. (2011). Computational morphology and the teaching of Semitic languages. Proceedings of the Second Workshop on Speech and Language Processing for Assistive Technologies (pp. 126–131). • Getachew, M. (2001). Automatic part of speech tagging for Amharic: An experiment using stochastic hidden Markov approach (master’s thesis). • Habash, N. & Rambow, O. (2005). Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. In Proceedings of ACL (pp. 573-580). • LFormat, K. D., Wang, L., & Wale, A. (2019). BiLSTM-CRF for Amharic part-of-speech tagging. Computing and Communications Workshop and Conference (CCWC), 2019 IEEE 9th Annual (pp. 660-663). • Mansur, N., Abraham, B., & Yaregal, A. (2009). Amharic verb lexicon in the context of machine translation. In Proceedings of the International Conference on Machine Learning and Cybernetics (pp. 1664–1671). • Ratnaparkhi, A. (1996). A maximum entropy model for part-of-speech tagging. In Proceedings of the Empirical Methods in Natural Language Processing (EMNLP). • Tach belie, M. Y., & Menzel, W. (2009). Amharic part-of-speech tagger for factored language model. In Proceedings of the International Conference on Machine Learning and Cybernetics (pp. 1711–1716). • Yimam, S. M. (2007). AMHARIC grammar. Addis Ababa, Ethiopia: Yimam Publishers. • Yimam, S. M. (2010). Automatic processing of Amharic: Tokenization, POS tagging, IR and MT (master’s thesis)
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- 12. 11 ❖ To be customize for actual usage. Interview Questions: 1. Can you please introduce yourself and your background in Amharic, Oromo, and Tigrinya language processing or related fields? 2. In your experience, what are the key challenges faced when working with large language models in Amharic, Afaan Oromoo, and Tigrinya language processing? 3. How do you perceive the current performance of existing large language models in processing texts in Ethiopian language (Amharic, Afaan Oromoo, and Tigrinya)? Are there any specific limitations or areas where they struggle? 4. What are the potential implications or consequences of these challenges in various domains, such as natural language understanding, machine translation, or sentiment analysis in Amharic, Afaan Oromoo, and Tigrinya? 5. In your opinion, what are the specific linguistic or cultural challenges in Ethiopian language (Amharic, Afaan Oromoo, and Tigrinya) that make it challenging for large language models to accurately process and understand? 6. Have you come across any notable instances where large language models have produced incorrect or inappropriate results when processing (Amharic, Afaan Oromoo, and Tigrinya) text? If yes, could you provide some examples? 7. Based on your expertise, what improvements or advancements do you think are necessary to enhance the performance of large language models in (Amharic, Afaan Oromoo, and Tigrinya) languages processing? 8. Are there any specific strategies or methodologies that you would recommend addressing the challenges faced by current language models in (Amharic, Afaan Oromoo, and Tigrinya) processing?