The predominance of English-language Web content creates an information-access barrier for speakers of low-resource languages such as Amharic.
This paper presents the design, implementation, and evaluation of a bidirectional Amharic-English cross-language question answering (CLQA) system that allows a user to formulate a natural-language question in either Amharic or English and retrieve an answer from documents written in the other language.
The system integrates a statistical machine translation (SMT) component for query translation, a maximum-entropy named entity recognition (NER) component for Amharic text processing, semantic-based indexing, question analysis, passage retrieval, and answer extraction.
A domain-diverse Amharic-English parallel corpus was used to train the SMT component, while monolingual cor-pora supported language modeling.
Documents were normalized, tokenized, stemmed, indexed, and semantically enriched before retrieval.
The original evaluation used precision and recall as the principal effectiveness measures.
For Amharic-to-English retrieval, the system achieved 72% precision and 79% recall; for English-to-Amharic retrieval, it achieved 65% precision and 70% recall.
These findings show that a modular translation-and-retrieval architecture can provide useful cross-language access even under low-resource conditions.
This revised article preserves the original experimental claims while situating them in the context of subsequent multilingual transformer and Amharic QA research.