Probabilistic Finite State Machines for Regression-based MT Evaluation
Mengqiu Wang, Christopher D. Manning · 2012
Accurate and robust metrics for automatic evaluation are key to the development of statistical machine translation (MT) systems. We first introduce a new regression model that uses a probabilistic finite state machine (pFSM) to compute weighted edit distance as predictions of translation quality. We also propose a novel pushdown automaton extension of the pFSM model for modeling word swapping and cross alignments that cannot be captured by standard edit distance models. Our models can easily incorporate a rich set of linguistic features, and automatically learn their weights, eliminating the need for ad-hoc parameter tuning. Our methods achieve state-of-the-art correlation with human judgments on two different prediction tasks across a diverse set of standard evaluations (NIST OpenMT06,08; WMT06-08). 1