Regular-pattern-sensitive CRFs for Distant Label Interactions

AbstractWhile LLMs have grown popular in sequence labeling, linear-chain conditionalrandom fields (CRFs) remain a popular alternativewith the ability to directly model interactions between labels.However, the Markov assumption limits them to interactions between adjacent labels.Weighted finite-state transducers (FSTs), in contrast, can modeldistant label–label interactions, but exact label inference is intractable in general.In this work, we present regular-pattern-sensitiveCRFs (RPCRFs), a method of enriching standardlinear-chain CRFs with the ability to learnlong-distance label interactions through user-specified patterns.This approach allows users to write regular-expressionlabel patterns concisely specifying which types of interactionsthe model should take into account, allowingthe model to learn from data whether and inwhich contexts these patterns occur. The resultcan be interpreted alternatively as a CRF augmented with additional,non-local potentials,or as a finite-state transducer whose structureis defined by a set of easily-interpretable patterns.Critically, exact training and inferenceare tractable for many pattern sets. We detailhow an RPCRF can be automatically constructed from a set of user-specified patterns,and demonstrate the model’s effectiveness ona sequence of three synthetic sequence modeling datasets.