Srihari Nanniyur

Ziheng Zeng, Kellen Tan Cheng, Srihari Venkat Nanniyur et al.

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.

Jianing Zhou, Hongyu Gong, Srihari Nanniyur et al.

We study a new application for text generation -- idiomatic sentence generation -- which aims to transfer literal phrases in sentences into their idiomatic counterparts. Inspired by psycholinguistic theories of idiom use in one's native language, we propose a novel approach for this task, which retrieves the appropriate idiom for a given literal sentence, extracts the span of the sentence to be replaced by the idiom, and generates the idiomatic sentence by using a neural model to combine the retrieved idiom and the remainder of the sentence. Experiments on a novel dataset created for this task show that our model is able to effectively transfer literal sentences into idiomatic ones. Furthermore, automatic and human evaluations show that for this task, the proposed model outperforms a series of competitive baseline models for text generation.