Multilingual Probing of Deep Pre-Trained Contextual Encoders

Abstract

Encoders that generate representations based on context have, in recent years, benefited from adaptations that allow for pre-training on large text corpora. Earlier work on evaluating fixed-length sentence representations has included the use of `probing' tasks, that use diagnostic classifiers to attempt to quantify the extent to which these encoders capture specific linguistic phenomena. The principle of probing has also resulted in extended evaluations that include relatively newer word-level pre-trained encoders. We build on probing tasks established in the literature and comprehensively evaluate and analyse -- from a typological perspective amongst others -- multilingual variants of existing encoders on probing datasets constructed for 6 non-English languages. Specifically, we probe each layer of a multiple monolingual RNN-based ELMo models, the transformer-based BERT's cased and uncased multilingual variants, and a variant of BERT that uses a cross-lingual modelling scheme (XLM).

Tasks

Reproductions