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We introduce semantic form mid-tuning, an approach for transferring semanticknowledge from semantic meaning representations into transformer-based languageencoders. In mid-tuning, we learn to align the text of general sentences -- nottied to any particular inference task -- and structured semanticrepresentations of those sentences. Our approach does not require goldannotated semantic representations. Instead, it makes use of automaticallygenerated semantic representations, such as from off-the-shelf PropBank andFrameNet semantic parsers. We show that this alignment can be learnedimplicitly via classification or directly via triplet loss. Our method yieldslanguage encoders that demonstrate improved predictive performance acrossinference, reading comprehension, textual similarity, and other semantic tasksdrawn from the GLUE, SuperGLUE, and SentEval benchmarks. We evaluate ourapproach on three popular baseline models, where our experimental results andanalysis concludes that current pre-trained language models can further benefitfrom structured semantic frames with the proposed mid-tuning method, as theyinject additional task-agnostic knowledge to the encoder, improving thegenerated embeddings as well as the linguistic properties of the given model,as evident from improvements on a popular sentence embedding toolkit and avariety of probing tasks.

Transferring Semantic Knowledge Into Language Encoders