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Multilingual Autoregressive Entity Linking (2022.tacl-1)
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Nicola De Cao, Ledell Wu, Kashyap Popat, Mikel Artetxe, Naman Goyal, Mikhail Plekhanov, Luke Zettlemoyer, Nicola Cancedda, Sebastian Riedel, Fabio Petroni
| Challenge: | mGENRE is a sequence-to-sequence system for multilingual entity linking . mGenRE is used to solve language-specific mentions to a multilingual Knowledge Base . |
| Approach: | They propose a sequence-to-sequence system for multilingual entity linking . they match language-specific mentions against a multilingual Knowledge Base (KB) mGENRE is a sequential system that predicts the name of the target entity token-by-token . |
| Outcome: | The proposed system improves on three popular MEL benchmarks and shows improvements in accuracy. |
Building an Efficient Multilingual Non-Profit IR System for the Islamic Domain Leveraging Multiprocessing Design in Rust (2024.emnlp-industry)
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| Challenge: | Existing models that are pre-trained on a general domain can deteriorate performance due to domain shift when applied to new domains. |
| Approach: | They propose to train a multilingual non-profit IR system for the Islamic domain using Rust Language capabilities. |
| Outcome: | The proposed model outperforms models pre-trained on general domains and on resource-constrained devices. |