Enhancing Machine Translation Experiences with Multilingual Knowledge Graphs
Abstract
Translating entity names, especially when a literal translation is not correct, poses a significant challenge. Although Machine Translation (MT) systems have achieved impressive results, they still struggle to translate cultural nuances and language-specific context. In this work, we show that the integration of multilingual knowledge graphs into MT systems can address this problem and bring two significant benefits: i) improving the translation of utterances that contain entities by leveraging their human-curated aliases from a multilingual knowledge graph, and, ii) increasing the interpretability of the translation process by providing the user with information from the knowledge graph.
Cite
Text
Conia et al. "Enhancing Machine Translation Experiences with Multilingual Knowledge Graphs." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30563Markdown
[Conia et al. "Enhancing Machine Translation Experiences with Multilingual Knowledge Graphs." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/conia2024aaai-enhancing/) doi:10.1609/AAAI.V38I21.30563BibTeX
@inproceedings{conia2024aaai-enhancing,
title = {{Enhancing Machine Translation Experiences with Multilingual Knowledge Graphs}},
author = {Conia, Simone and Lee, Daniel and Li, Min and Minhas, Umar Farooq and Li, Yunyao},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23781-23783},
doi = {10.1609/AAAI.V38I21.30563},
url = {https://mlanthology.org/aaai/2024/conia2024aaai-enhancing/}
}