Gradient Computation In Linear-Chain Conditional Random Fields Using The Entropy Message Passing Algorithm

TL;DR

This paper introduces a numerically stable, memory-efficient recursive algorithm for computing gradients in linear-chain CRFs, especially effective for long sequences, by operating over the log-domain expectation semiring.

Contribution

It presents a novel forward algorithm that reduces memory complexity independent of sequence length, improving gradient computation for long sequences in CRFs.

Findings

Algorithm is numerically stable and memory-efficient.

Effective for long observation sequences.

Outperforms traditional methods in experiments.

Abstract

The paper proposes a numerically stable recursive algorithm for the exact computation of the linear-chain conditional random field gradient. It operates as a forward algorithm over the log-domain expectation semiring and has the purpose of enhancing memory efficiency when applied to long observation sequences. Unlike the traditional algorithm based on the forward-backward recursions, the memory complexity of our algorithm does not depend on the sequence length. The experiments on real data show that it can be useful for the problems which deal with long sequences.