  Natural language understanding (NLU) is an essential branch of natural
language processing, which relies on representations generated by pre-trained
language models (PLMs). However, PLMs primarily focus on acquiring
lexico-semantic information, while they may be unable to adequately handle the
meaning of constructions. To address this issue, we introduce construction
grammar (CxG), which highlights the pairings of form and meaning, to enrich
language representation. We adopt usage-based construction grammar as the basis
of our work, which is highly compatible with statistical models such as PLMs.
Then a HyCxG framework is proposed to enhance language representation through a
three-stage solution. First, all constructions are extracted from sentences via
a slot-constraints approach. As constructions can overlap with each other,
bringing redundancy and imbalance, we formulate the conditional max coverage
problem for selecting the discriminative constructions. Finally, we propose a
relational hypergraph attention network to acquire representation from
constructional information by capturing high-order word interactions among
constructions. Extensive experiments demonstrate the superiority of the
proposed model on a variety of NLU tasks.

Computation and Language