On Generality and Knowledge Transferability in Cross-Domain Duplicate Question Detection for Heterogeneous Community Question Answering
Duplicate question detection is an ongoing challenge in community question answering because semantically equivalent questions can have significantly different words and structures. In addition, the identification of duplicate questions can reduce the resources required for retrieval, when the same questions are not repeated. This study compares the performance of deep neural networks and gradient tree boosting, and explores the possibility of domain adaptation with transfer learning to improve the under-performing target domains for the text-pair duplicates classification task, using three heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and academic English Stack Exchange. Ultimately, our study exposes the alternative hypothesis that the meaning of a "duplicate" is not inherently general-purpose, but rather is dependent on the domain of learning, hence reducing the chance of transfer learning through adapting to the domain.
Code (0)
등록된 구현이 없습니다.
Tasks
Community Question AnsweringDomain AdaptationGeneral ClassificationQuestion AnsweringRetrievalTransfer LearningSimilar Papers 제목 키워드 기반
Precise Action-to-Video Generation Through Visual Action Prompts
We present visual action prompts, a unified action representation for action-to-video generation of complex high-DoF interactions while maintaining transferable visual dynamics across domains. Action-driven video generat…
Video GenerationMulti-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
Knowledge graphs (KGs) provide reliable external knowledge for a wide variety of AI tasks in the form of structured triples. Knowledge graph pre-training (KGP) aims to pre-train neural networks on large-scale KGs and pro…
Knowledge GraphsPrompt LearningContrastive Entity Linkage: Mining Variational Attributes from Large Catalogs for Entity Linkage
Presence of near identical, but distinct, entities called entity variations makes the task of data integration challenging. For example, in the domain of grocery products, variations share the same value for attributes s…
Data IntegrationTowards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and sentences in text. In contrast, discoveri…
Graph Neural NetworkIn-Context LearningZero-shot GeneralizationZero-Shot LearningTrustworthy Transfer Learning: A Survey
Transfer learning aims to transfer knowledge or information from a source domain to a relevant target domain. In this paper, we understand transfer learning from the perspectives of knowledge transferability and trustwor…
Privacy PreservingSurveyTransfer Learning