  Keyphrase generation aims to automatically generate short phrases summarizing
an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021)
generates keyphrases as a set and has achieved competitive performance.
Nevertheless, we observe serious calibration errors outputted by ONE2SET,
especially in the over-estimation of $\varnothing$ token (means "no
corresponding keyphrase"). In this paper, we deeply analyze this limitation and
identify two main reasons behind: 1) the parallel generation has to introduce
excessive $\varnothing$ as padding tokens into training instances; and 2) the
training mechanism assigning target to each slot is unstable and further
aggravates the $\varnothing$ token over-estimation. To make the model
well-calibrated, we propose WR-ONE2SET which extends ONE2SET with an adaptive
instance-level cost Weighting strategy and a target Re-assignment mechanism.
The former dynamically penalizes the over-estimated slots for different
instances thus smoothing the uneven training distribution. The latter refines
the original inappropriate assignment and reduces the supervisory signals of
over-estimated slots. Experimental results on commonly-used datasets
demonstrate the effectiveness and generality of our proposed paradigm.
