Triangular-Chain Conditional Random Fields

Abstract

Sequential modeling is a fundamental task in scientific fields, especially in speech and natural language processing, where many problems of sequential data can be cast as a sequential labeling or a sequence classification. In many applications, the two problems are often correlated, for example named entity recognition and dialog act classification for spoken language understanding. This paper presents triangular-chain conditional random fields (CRFs), a unified probabilistic model combining two related problems. Triangular-chain CRFs jointly represent the sequence and meta-sequence labels in a single graphical structure that both explicitly encodes their dependencies and preserves uncertainty between them. An efficient inference and parameter estimation method is described for triangular-chain CRFs by extending linear-chain CRFs. This method outperforms baseline models on synthetic data and real-world dialog data for spoken language understanding.