Neural Net Models of Open-domain Discourse Coherence
Table 4: Adversarial Success for different models.
['Model', 'adver-1', 'adver-2', 'adver-3']
As can be seen, the latent variable model VLV-GM is able to generate chunk of texts that are most indistinguishable from coherent texts from humans. This is due to its ability to handle the dependency between neighboring sentences. Performance declines as the number of turns increases due to the accumulation of errors and current models’ inability to model long-term sentence-level dependency. All models perform poorly on the adver-3 evaluation metric, with the best adversarial success value being 0.081 (the trained evaluator is able to distinguish between human-generated and machine generated dialogues with greater than 90 percent accuracy for all models).