Gradient-based Inference for Networks with Output Constraints
Table 11: Comparison of runtime for difference inference procedures in the noise-free constraint setting: Viterbi, A*[He et al.2017] and GBI. For SRL-100 refer Table 1 and SRL-NW is a model trained on NW genre.
['Network', 'Genre(s)', 'No. of examples', 'Failure rate (%)', 'Inference time (approx. mins) Viterbi', 'Inference time (approx. mins) GBI', 'Inference time (approx. mins) A*']
In general, we observe that GBI tends to be faster than A*, especially when the dataset is large enough. One exception is the BC domain where GBI is just slightly faster than A *. We hypothesize it might be due to the difficulty of the constraint violations as its failure rate is higher than usual. GBI will spend more time searching for the correct output (more iterations) if it is harder to find the solution.