An Overview of "General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation"
The paper "General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation" addresses a salient challenge in keyphrase generation (KPG): the domain transferability of models. While KPG systems have seen significant advancements through deep neural networks and large datasets, their performance remains largely constricted within the domains of training data. The paper introduces a novel methodology designed to overcome this limitation, enhancing cross-domain adaptability while minimizing the reliance on domain-specific annotated data.
Key Contributions and Methodology
The authors begin by highlighting the substantial distribution shifts encountered when KPG models trained on one domain are applied to others. This observation underscores the necessity for strategies that enable more effective domain transfer. The proposed solution is a three-stage pipeline that incrementally steers the learning process from general syntactical features to domain-specific semantics, facilitating a more adaptable model architecture.
- Domain-General Phrase Pre-training: The first stage involves pre-training Sequence-to-Sequence models with widely available generic phrase annotations sourced from online data, such as Wikipedia. By focusing on general phraseness in this preliminary phase, the models develop a broad capability for generating syntactically accurate phrases across diverse contexts.
- Transfer Labeling for Domain Adaptation: This innovative self-training stage uses the pretrained model to generate domain-specific pseudo keyphrases, adapting the model to new domains without requiring manual annotations. The method iteratively refines the model by using its own predictions as self-supervision, which the authors term "Transfer Labeling."
- Low-resource Fine-Tuning: Finally, the model is fine-tuned using a limited set of true labels from the target domain. This step further anchors the model in the specific semantic nuances of the domain, allowing it to generate high-quality, contextually relevant keyphrases with minimal annotated data.
Experimental Results and Analysis
Empirical validation on datasets spanning diverse domains, including scientific papers, news articles, and community forums, demonstrates that the three-stage approach consistently boosts the performance of KPG models. The proposed framework achieves sustainable improvements even when adaptation is accomplished with limited in-domain annotated data. Notably, the experiments reveal that models initialized with pre-trained LLMs like BART exhibit enhanced robustness and adaptability.
In testing on domains divergent from the pre-training corpora, the Transfer Labeling method proves particularly beneficial. Its capability to bootstrap from unlabeled in-domain data significantly mitigates the need for costly annotations. Moreover, the experimentation with combining transfer labeling and random span strategies suggests opportunities for further optimizing domain adaptation techniques through data augmentation schemas.
Implications and Future Directions
This research contributes both practically and theoretically to the field of keyphrase generation. Practically, it offers a scalable approach to domain adaptation that can be readily integrated with existing models, reducing resource dependency and expanding accessibility. Theoretically, it introduces a nuanced understanding of domain knowledge as it relates to keyness and phraseness, setting a foundation for future exploration into disentangling these aspects within broader natural language processing contexts.
A potential avenue for future research could involve exploring the incorporation of additional domain adaptation strategies, such as soft-labeling or model distillation techniques, to further enhance the robustness and generalization capabilities of KPG models. Additionally, examining the application of this methodology in other NLP tasks such as text classification or information retrieval could yield valuable insights into the generalizability of the General-to-Specific Transfer Labeling paradigm.
In conclusion, this paper presents a substantive and methodically sound approach to addressing domain adaptability in keyphrase generation, with promising implications for its scalability and applicability across varying domains and datasets.