Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable

Philipp Dufter, Mengjie Zhao, Hinrich Schütze

Word embeddings induced from local context are prevalent in NLP. A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) method. Another line of work induces high-performing multilingual embeddings from concepts (Dufter et al., 2018). In this paper, we propose Co+Co, a simple and scalable method that combines context-based and concept-based learning. From a sentence aligned corpus, concepts are extracted via sampling; words are then associated with their concept ID and sentence ID in embedding learning. This is the first work that successfully combines context-based and concept-based embedding learning. We show that Co+Co performs well for two different application scenarios: the Parallel Bible Corpus (1000+ languages, low-resource) and EuroParl (12 languages, high-resource). Among methods applicable to both corpora, Co+Co performs best in our evaluation setup of six tasks.

Knowledge Graph

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