On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation

Di Wu, Wasi Uddin Ahmad, and Kai-Wei Chang, in LREC-COLING, 2024.

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Abstract

This study addresses the application of encoder-only Pre-trained Language Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of domain-tailored encoder-only models compared to encoder-decoder models. We investigate three core inquiries: (1) the efficacy of encoder-only PLMs in KPG, (2) optimal architectural decisions for employing encoder-only PLMs in KPG, and (3) a performance comparison between in-domain encoder-only and encoder-decoder PLMs across varied resource settings. Our findings, derived from extensive experimentation in two domains reveal that with encoder-only PLMs, although KPE with Conditional Random Fields slightly excels in identifying present keyphrases, the KPG formulation renders a broader spectrum of keyphrase predictions. Additionally, prefix-LM fine-tuning of encoder-only PLMs emerges as a strong and data-efficient strategy for KPG, outperforming general-domain seq2seq PLMs. We also identify a favorable parameter allocation towards model depth rather than width when employing encoder-decoder architectures initialized with encoder-only PLMs. The study sheds light on the potential of utilizing encoder-only PLMs for advancing KPG systems and provides a groundwork for future KPG methods.

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@inproceedings{wu2024leveraging,
  booktitle = {LREC-COLING},
  year = {2024},
  title = {On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation},
  author = {Wu, Di and Ahmad, Wasi Uddin and Chang, Kai-Wei}
}