Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages

Ralethe, Sello and Buys, Jan (2025) Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages, Proceedings of International Conference on Computational Linguistics (COLING 2025), 19-24 January 2025, bu Dhabi, UAE.

Text COLING_2025_Proceedings_LeNS_Align_final.pdf Download (255kB)

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.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence > Natural language processing Computing methodologies > Artificial intelligence > Natural language processing > Language resources
Date Deposited:	24 Dec 2024 12:04
Last Modified:	24 Dec 2024 12:04
URI:	https://pubs.cs.uct.ac.za/id/eprint/1712

Available Versions of this Item

• Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages. (deposited 10 Dec 2024 07:27)
  • Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages. (deposited 24 Dec 2024 12:04) [Currently Displayed]

Actions (login required)

View Item