# Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets

arXiv: https://arxiv.org/abs/2011.01154 · HTML: https://mlsanity.com/paper/2011.01154/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 12 of 12)

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- [AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text](https://mlsanity.com/paper/2503.18247/) (2025)
- [Dense Retrieval for Low Resource Languages -- the Case of Amharic Language](https://mlsanity.com/paper/2503.18570/) (2025)
- [MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness](https://mlsanity.com/paper/2403.14990/) (2024)
- [A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge'ez Script](https://mlsanity.com/paper/2507.15142/) (2025)
- [Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages](https://mlsanity.com/paper/2607.12612/) (2026)

## Abstract (arXiv)

The availability of different pre-trained semantic models enabled the quick development of machine learning components for downstream applications. Despite the availability of abundant text data for low resource languages, only a few semantic models are publicly available. Publicly available pre-trained models are usually built as a multilingual version of semantic models that can not fit well for each language due to context variations. In this work, we introduce different semantic models for Amharic. After we experiment with the existing pre-trained semantic models, we trained and fine-tuned nine new different models using a monolingual text corpus. The models are build using word2Vec embeddings, distributional thesaurus (DT), contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and investigate their impact. We find that newly trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from RoBERTA perform better than the word2Vec models.