Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation
Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, Nuno M Guerreiro
Abstract
Hallucinated translations pose significant threats and safety concerns when it comes to practical deployment of machine translation systems. Previous research works have identified that detectors exhibit complementary performance — different detectors excel at detecting different types of hallucinations. In this paper, we propose to address the limitations of individual detectors by combining them and introducing a straightforward method for aggregating multiple detectors. Our results demonstrate the efficacy of our aggregated detector, providing a promising step towards evermore reliable machine translation systems.- Anthology ID:
- 2024.emnlp-main.1033
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 18573–18583
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-main.1033
- DOI:
- 10.18653/v1/2024.emnlp-main.1033
- Cite (ACL):
- Anas Himmi, Guillaume Staerman, Marine Picot, Pierre Colombo, and Nuno M Guerreiro. 2024. Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 18573–18583, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Enhanced Hallucination Detection in Neural Machine Translation through Simple Detector Aggregation (Himmi et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.1033.pdf