Posterior Control of Blackbox Generation
Table 3: Automatic metrics for text generation. ∗ marks systems without learned control states. (Left) E2E. Comparison of systems from dusek-jurcicek-2016-sequence,template-generation,shen-etal-2019-pragmatically, our model and ablations. (Right) WikiBio. Comparison of template-generation, Liu-seq2seq, wikibiodata and our full model.
['[EMPTY]', 'E2E BLEU', 'E2E NIST', 'E2E ROUGE', 'E2E CIDEr', 'E2E MET']
On E2E (left), our posterior control model outperforms the neural benchmark system on all validation metrics and most of the test metrics. It also achieves results comparable or better than a specialized encoder-decoder system. It has significantly better performance than the controllable NTemp and NTemp+AR in all metrics on both validation and test. This demonstrates that the PC model provides interpretable and controllable states without sacrificing any representation power or generation performance.