WR-One2Set: Towards Well-Calibrated Keyphrase Generation
Binbin Xie, Xiangpeng Wei, Baosong Yang, Huan Lin, Jun Xie, Xiaoli Wang, Min Zhang, Jinsong Su · 2022
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 ∅ 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 ∅ as padding tokens into training instances; and 2) the training mechanism assigning target to each slot is unstable and further aggravates the ∅ token over-estimation.To make the model wellcalibrated, we propose WR-ONE2SET which extends ONE2SET with an adaptive instancelevel cost Weighting strategy and a target Reassignment 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.