Mikhail Plekhanov
2022
Multilingual Autoregressive Entity Linking
Nicola De Cao
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Ledell Wu
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Kashyap Popat
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Mikel Artetxe
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Naman Goyal
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Mikhail Plekhanov
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Luke Zettlemoyer
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Nicola Cancedda
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Sebastian Riedel
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Fabio Petroni
Transactions of the Association for Computational Linguistics, Volume 10
We present mGENRE, a sequence-to- sequence system for the Multilingual Entity Linking (MEL) problem—the task of resolving language-specific mentions to a multilingual Knowledge Base (KB). For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token in an autoregressive fashion. The autoregressive formulation allows us to effectively cross-encode mention string and entity names to capture more interactions than the standard dot product between mention and entity vectors. It also enables fast search within a large KB even for mentions that do not appear in mention tables and with no need for large-scale vector indices. While prior MEL works use a single representation for each entity, we match against entity names of as many languages as possible, which allows exploiting language connections between source input and target name. Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time. This leads to over 50% improvements in average accuracy. We show the efficacy of our approach through extensive evaluation including experiments on three popular MEL benchmarks where we establish new state-of-the-art results. Source code available at https://github.com/facebookresearch/GENRE.
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Co-authors
- Nicola De Cao 1
- Ledell Wu 1
- Kashyap Popat 1
- Mikel Artetxe 1
- Naman Goyal 1
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