Masked language models have quickly become the de facto standard when processing text. Recently, several approaches have been proposed to further enrich word representations with external knowledge sources such as knowledge graphs. However, these models are devised and evaluated in a monolingual setting only. In this work, we propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap across different languages by means of a shared vocabulary of entities. We show that our approach effectively injects new lexical-semantic knowledge into neural models, improving their performance on different semantic tasks in the zero-shot crosslingual setting. As an additional advantage, our intermediate training does not require any supplementary input, allowing our models to be applied to new datasets right away. In our experiments, we use Wikipedia articles in up to 100 languages and already observe consistent gains compared to strong baselines when predicting entities using only the English Wikipedia. Further adding extra languages lead to improvements in most tasks up to a certain point, but overall we found it non-trivial to scale improvements in model transferability by training on ever increasing amounts of Wikipedia languages.

{W}ikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting {W}ikipedia Hyperlinks / Calixto, Iacer; Raganato, Alessandro; Pasini, Tommaso. - (2021), pp. 3651-3661. (Intervento presentato al convegno the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies tenutosi a Online; Online).

{W}ikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting {W}ikipedia Hyperlinks

Alessandro Raganato;Tommaso Pasini
2021

Abstract

Masked language models have quickly become the de facto standard when processing text. Recently, several approaches have been proposed to further enrich word representations with external knowledge sources such as knowledge graphs. However, these models are devised and evaluated in a monolingual setting only. In this work, we propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap across different languages by means of a shared vocabulary of entities. We show that our approach effectively injects new lexical-semantic knowledge into neural models, improving their performance on different semantic tasks in the zero-shot crosslingual setting. As an additional advantage, our intermediate training does not require any supplementary input, allowing our models to be applied to new datasets right away. In our experiments, we use Wikipedia articles in up to 100 languages and already observe consistent gains compared to strong baselines when predicting entities using only the English Wikipedia. Further adding extra languages lead to improvements in most tasks up to a certain point, but overall we found it non-trivial to scale improvements in model transferability by training on ever increasing amounts of Wikipedia languages.
2021
the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
transformer; language model; deep learning; entity linking; multilingual
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
{W}ikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting {W}ikipedia Hyperlinks / Calixto, Iacer; Raganato, Alessandro; Pasini, Tommaso. - (2021), pp. 3651-3661. (Intervento presentato al convegno the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies tenutosi a Online; Online).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1551127
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