  Using prompts to explore the knowledge contained within pre-trained language
models for downstream tasks has now become an active topic. Current prompt
tuning methods mostly convert the downstream tasks to masked language modeling
problems by adding cloze-style phrases and mapping all labels to verbalizations
with fixed length, which has proven effective for tasks with simple label
spaces. However, when applied to relation classification exhibiting complex
label spaces, vanilla prompt tuning methods may struggle with label
verbalizations with arbitrary lengths due to rigid prompt restrictions.
Inspired by the text infilling task for pre-training generative models that can
flexibly predict missing spans, we propose a novel generative prompt tuning
method to reformulate relation classification as an infilling problem, which
frees our approach from limitations of current prompt based approaches and thus
fully exploits rich semantics of entity and relation types. In addition, we
design entity-guided decoding and discriminative relation scoring to generate
and align relations effectively and efficiently during inference. Extensive
experiments under fully supervised settings and low-resource settings
demonstrate the effectiveness of our approach.
