Generalized Tuning of Distributional Word Vectors for
Monolingual and Cross-Lingual Lexical Entailment
Abstract
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