We propose the chainization algorithm for effective order learning when only partially ordered data are available. First, we develop a binary comparator to predict missing ordering relations between instances. Then, by extending the Kahn’s algorithm, we form a chain representing a linear ordering of instances. We fine-tune the comparator over pseudo pairs, which are sampled from the chain, and then re-estimate the linear ordering alternately. As a result, we obtain a more reliable comparator and a more meaningful linear ordering. Experimental results show that the proposed algorithm yields excellent rank estimation performances under various weak supervision scenarios, including semi-supervised learning, domain adaptation, and bipartite cases. The source codes are available at https://github.com/seon92/Chainization.
CITATION STYLE
Lee, S. H., & Kim, C. S. (2022). Order Learning Using Partially Ordered Data via Chainization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13673 LNCS, pp. 196–211). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19778-9_12
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