Generalized chart constraints for efficient PCFG and TAG parsing
Figure 4: Results for TAG parsing.
['[EMPTY]', 'Parser', 'f-score', 'time', 'speedup', '% gold']
This grammar struggles with unseen words, and thus achieves a rather low f-score (see Fig. Chart constraints by themselves speed the TAG parser up by 3.8x, almost matching the performance of gold chart constraints. This improvement is remarkable in that \newciteirtg-ctf-17 found that coarse-to-fine parsing, which also prunes the substrings a finer-grained parser considers, did not improve TAG parsing performance. The sentence-specific grammars are so small that we can parse the test corpus without binarizing them. As Fig. (k=3); the use of word embeddings boosts the coverage to almost 100% and the f-score to around 80. Adding chart constraints on top of supertagging further improves the parser, yielding the best speed (at k=3) and accuracy (at k=10). We achieve an overall speedup of two orders of magnitude with a drastic increase in accuracy. As the results in Fig. This is to be expected, because CC has more opportunities to prune chart items early, but false positive chart constraints can cause it to overprune. When θ is scaled so both strategies achieve the same accuracy – i.e., B/E θ=0.8 for CC θ=0.95, or CC θ=0.99 for B/E θ=0.95 –, CC is faster than B /E. This suggests that imposing chart constraints on the gap is beneficial and illustrates the flexibility and power of the “admissible items” approach we introduce here.