In this work, we explore how to train task-specific language models aimed
towards learning rich representation of keyphrases from text documents. We
experiment with different masking strategies for pre-training transformer
language models (LMs) in discriminative as well as generative settings. In the
discriminative setting, we introduce a new pre-training objective - Keyphrase
Boundary Infilling with Replacement (KBIR), showing large gains in performance
(upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is
fine-tuned for the task of keyphrase extraction. In the generative setting, we
introduce a new pre-training setup for BART - KeyBART, that reproduces the
keyphrases related to the input text in the CatSeq format, instead of the
denoised original input. This also led to gains in performance (upto 4.33
points in F1@M) over SOTA for keyphrase generation. Additionally, we also
fine-tune the pre-trained language models on named entity recognition (NER),
question answering (QA), relation extraction (RE), abstractive summarization
and achieve comparable performance with that of the SOTA, showing that learning
rich representation of keyphrases is indeed beneficial for many other
fundamental NLP tasks.

本文研究如何训练针对文本文档中关键短语的任务特定语言模型，通过使用不同的掩码策略在区分性和生成性环境下对预训练变压器语言模型进行实验，并提出新的预训练目标 - 关键短语边界填充替换（KBIR），用于关键短语提取，与现有最佳方法相比，能够提高 8.16 个 F1 分数；此外，还提出了适用于 BART 的新的预训练设置 - KeyBART，能够在 CatSeq 格式中重现与输入文本相关的关键短语，用于关键短语生成，并能比现有最佳方法提高 4.33 个 F1M 分数。最后，对预训练语言模型进行细微调整，用于命名实体识别，问答，关系抽取，编写摘要等，达到了与现有最佳方法可比的性能，表明学习关键短语的丰富表达对于许多其他基础的自然语言处理任务是有益的。