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    <abstract>Knowledge bases (KBs) in low-resource languages (LRLs) are often incomplete, posing a challenge for developing effective question answering systems over KBs in those languages. On the other hand, the size of training corpora for LRL language models is also limited, restricting the ability to do zero-shot question answering using multilingual language models. To address these issues, we propose a two-fold approach. First, we introduce LeNS-Align, a novel cross-lingual mapping technique which improves the quality of word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment. LeNS-Align is applied to perform cross-lingual projection of KB triples. Second, we leverage the projected KBs to enhance multilingual language models’ question answering capabilities by augmenting the models with Graph Neural Networks embedding the projected knowledge. We apply our approach to map triples from two existing English KBs, ConceptNet and DBpedia, to create comprehensive LRL knowledge bases for four low-resource South African languages. Evaluation on three translated test sets show that our approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access. The results highlight how our approach contributes to bridging the knowledge gap for low-resource languages by expanding knowledge coverage and question answering capabilities.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages
%A Ralethe, Sello
%A Buys, Jan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ralethe-buys-2025-cross
%X Knowledge bases (KBs) in low-resource languages (LRLs) are often incomplete, posing a challenge for developing effective question answering systems over KBs in those languages. On the other hand, the size of training corpora for LRL language models is also limited, restricting the ability to do zero-shot question answering using multilingual language models. To address these issues, we propose a two-fold approach. First, we introduce LeNS-Align, a novel cross-lingual mapping technique which improves the quality of word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment. LeNS-Align is applied to perform cross-lingual projection of KB triples. Second, we leverage the projected KBs to enhance multilingual language models’ question answering capabilities by augmenting the models with Graph Neural Networks embedding the projected knowledge. We apply our approach to map triples from two existing English KBs, ConceptNet and DBpedia, to create comprehensive LRL knowledge bases for four low-resource South African languages. Evaluation on three translated test sets show that our approach improves zero-shot question answering accuracy by up to 17% compared to baselines without KB access. The results highlight how our approach contributes to bridging the knowledge gap for low-resource languages by expanding knowledge coverage and question answering capabilities.
%U https://aclanthology.org/2025.coling-main.675/
%P 10111-10124

Markdown (Informal)

[Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages](https://aclanthology.org/2025.coling-main.675/) (Ralethe & Buys, COLING 2025)

• Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages (Ralethe & Buys, COLING 2025)

ACL

• Sello Ralethe and Jan Buys. 2025. Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10111–10124, Abu Dhabi, UAE. Association for Computational Linguistics.