Specialising Word Vectors for Lexical Entailment
(c) Summary
['[EMPTY]', 'All']
As shown by the high inter-annotator agreement on HyperLex (0.85), humans are able to consistently reason about graded le. However, current state-of-the-art representation architectures are far from this ceiling. For instance, \newciteVulic:2017cl evaluate a plethora of architectures and report a high-score of only 0.320 Two recent representation models Nickel and Kiela (on the noun subset).