Training keyphrase generation (KPG) models require a large amount of annotated data, which can be prohibitively expensive and often limited to specific domains. In this study, we first demonstrate that large distribution shifts among different domains severely hinder the transferability of KPG models. We then propose a three-stage pipeline, which gradually guides KPG models' learning focus from general syntactical features to domain-related semantics, in a data-efficient manner. With Domain-general Phrase pre-training, we pre-train Sequence-to-Sequence models with generic phrase annotations that are widely available on the web, which enables the models to generate phrases in a wide range of domains. The resulting model is then applied in the Transfer Labeling stage to produce domain-specific pseudo keyphrases, which help adapt models to a new domain. Finally, we fine-tune the model with limited data with true labels to fully adapt it to the target domain. Our experiment results show that the proposed process can produce good-quality keyphrases in new domains and achieve consistent improvements after adaptation with limited in-domain annotated data. All code and datasets are available at https://github.com/memray/OpenNMT-kpg-release.

翻译：训练关键短语生成（KPG）模型需要大量带标注的数据，这可能导致极其高昂的成本且通常局限于特定领域。在本研究中，我们首先证明不同领域间显著的分布偏移严重阻碍了KPG模型的可迁移性。随后，我们提出一种三阶段流水线方法，以数据高效的方式逐步引导KPG模型的学习焦点从通用句法特征转向领域相关语义。具体地，通过领域通用短语预训练，我们利用网络中广泛可得的通用短语标注对序列到序列模型进行预训练，使模型能够生成跨多领域的关键短语。所得模型随后在迁移标注阶段被用于生成领域特定的伪关键短语，从而帮助模型适应新领域。最终，我们利用少量带真实标签的数据对模型进行微调，使其完全适配目标领域。实验结果表明，所提过程能在新领域生成高质量的关键短语，且在使用有限领域内标注数据进行适配后能实现持续的性能提升。所有代码和数据集均可在https://github.com/memray/OpenNMT-kpg-release获取。