Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment
Goran Glavaš, Ivan Vulić · 2019
Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation.In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE.Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space.Yet -unlike existing retrofitting models -it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints.Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection.We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20% in accuracy) over state-ofthe-art in cross-lingual LE detection.