Syntactically Look-Ahead Attention Network for Sentence Compression
Table 3: Results on the BNC Corpus. † indicates the same as in Table 2.
['Tagger', 'Tagger', '[BOLD] F1 54.6', '[BOLD] R-1 36.8', '[BOLD] R-2 27.7', '[BOLD] R-L 36.4', '[BOLD] ΔC -39.1']
We can clearly observe that SLAHAN w/ syn outperforms other models for all metrics. Comparing between Base, Parent, Child and SLAHAN, we can understand that SLAHAN w/ syn captured important words during the decoding step even in the BNC corpus. The remarkable performance of SLAHAN w/ syn supports the effectiveness of explicit syntactic information. That is, in the out-of-domain dataset, the dependency graph learned with implicit syntactic information obtained lower scores than that learned with explicit syntactic information. The result agrees with the findings of the previous research [wang-EtAl:2017:Long4]. From these results, we can conclude that SLAHAN is effective even for both long and out-of-domain sentences.