TA-DA: Topic-Aware Domain Adaptation for Scientific Keyphrase Identification and Classification (Student Abstract)
This work addresses keyphrase identification for scientific documents, presenting an incremental improvement.
The paper tackled keyphrase extraction from scientific documents by introducing TA-DA, a topic-aware domain adaptation framework, which improved performance by up to 5% in F1-score exact match over baselines.
Keyphrase identification and classification is a Natural Language Processing and Information Retrieval task that involves extracting relevant groups of words from a given text related to the main topic. In this work, we focus on extracting keyphrases from scientific documents. We introduce TA-DA, a Topic-Aware Domain Adaptation framework for keyphrase extraction that integrates Multi-Task Learning with Adversarial Training and Domain Adaptation. Our approach improves performance over baseline models by up to 5% in the exact match of the F1-score.