Abstract
Grounded in cognitive linguistics, graded lexical entailment (GR-LE) is concerned with finegrained assertions regarding the directional hierarchical relationships between concepts on
a continuous scale. In this paper, we present
the first work on cross-lingual generalisation
of GR-LE relation. Starting from HyperLex, the only available GR-LE dataset in English, we construct new monolingual GR-LE
datasets for three other languages, and combine those to create a set of six cross-lingual
GR-LE datasets termed CL-HYPERLEX. We
next present a novel method dubbed CLEAR
(Cross-Lingual Lexical Entailment AttractRepel) for effectively capturing graded (and binary) LE, both monolingually in different languages as well as across languages (i.e., on CLHYPERLEX). Coupled with a bilingual dictionary, CLEAR leverages taxonomic LE knowledge in a resource-rich language (e.g., English) and propagates it to other languages.
Supported by cross-lingual LE transfer, CLEAR
sets competitive baseline performance on three
new monolingual GR-LE datasets and six
cross-lingual GR-LE datasets. In addition, we
show that CLEAR outperforms current state-ofthe-art on binary cross-lingual LE detection by
a wide margin for diverse language pairs.