  We introduce semantic form mid-tuning, an approach for transferring semantic
knowledge from semantic meaning representations into transformer-based language
encoders. In mid-tuning, we learn to align the text of general sentences -- not
tied to any particular inference task -- and structured semantic
representations of those sentences. Our approach does not require gold
annotated semantic representations. Instead, it makes use of automatically
generated semantic representations, such as from off-the-shelf PropBank and
FrameNet semantic parsers. We show that this alignment can be learned
implicitly via classification or directly via triplet loss. Our method yields
language encoders that demonstrate improved predictive performance across
inference, reading comprehension, textual similarity, and other semantic tasks
drawn from the GLUE, SuperGLUE, and SentEval benchmarks. We evaluate our
approach on three popular baseline models, where our experimental results and
analysis concludes that current pre-trained language models can further benefit
from structured semantic frames with the proposed mid-tuning method, as they
inject additional task-agnostic knowledge to the encoder, improving the
generated embeddings as well as the linguistic properties of the given model,
as evident from improvements on a popular sentence embedding toolkit and a
variety of probing tasks.
