Approximation-Aware Dependency Parsing by Belief Propagation
Table 1: The impact of exact vs. approximate inference on a 2nd-order model with grandparent factors only. Results are for the development (§ 22) and test (§ 23) sections of PTB-YM.
['Train', 'Inference', 'Dev UAS', 'Test UAS']
time is possible using the dynamic programming algorithm for Model 0 of \newcitekoo_efficient_2010. The training approaches are CLL and approximation-aware inference with an L2 objective. The inference methods are BP with only four iterations or exact inference by dynamic programming. On test UAS, we find that both the CLL and L2 parsers with exact inference outperform approximate inference—though the margin for CLL is much larger. Surprisingly, our L2-trained parser, which uses only 4 iterations of BP and O(n3) runtime, does just as well as CLL with exact inference. Our L2 parser with exact inference performs the best.