2014/04/25 by Arvind Agarwal, Agarwal, Arvind, Saurabh Kataria +1
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Music and Audio Processing #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1404.6580
openalex publication_date 2014/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we present a learning method for sequence labeling tasks in which each example sequence has multiple label sequences. Our method learns multiple models, one model for each label sequence. Each model computes the joint probability of all label sequences given the example sequence. Although each model considers all label sequences, its primary focus is only one label sequence, and therefore, each model becomes a task-specific model, for the task belonging to that primary label. Such multiple models are learned \it simultaneously by facilitating the learning transfer among models through \it explicit parameter sharing. We experiment the proposed method on two applications and show that our method significantly outperforms the state-of-the-art method.