IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions
Ziheng Zeng Kellen Tan Cheng Srihari Venkat Nanniyur Jianing Zhou Suma Bhat
Idiomatic expression (IE) processing and comprehension have challenged
pre-trained language models (PTLMs) because their meanings are
non-compositional. Unlike prior works that enable IE comprehension through
fine-tuning PTLMs with sentences containing IEs, in this work, we construct
IEKG, a commonsense knowledge graph for figurative interpretations of IEs. This
extends the established ATOMIC2020 graph, converting PTLMs into knowledge
models (KMs) that encode and infer commonsense knowledge related to IE use.
Experiments show that various PTLMs can be converted into KMs with IEKG. We
verify the quality of IEKG and the ability of the trained KMs with automatic
and human evaluation. Through applications in natural language understanding,
we show that a PTLM injected with knowledge from IEKG exhibits improved IE
comprehension ability and can generalize to IEs unseen during training.