CoCon: A Self-Supervised Approach for Controlled Text Generation
Table 3: Evaluation of topic-controlled generations. Topic accuracy report ratio of samples that were classified as their target topic.
['Model', 'Topic %', 'Perplexity', 'Dist-1', 'Dist-2', 'Dist-3']
CoCon’s generated texts appear to be more relevant to the target topic than PPLM and CTRL. Rather than the more local content control of CoCon, the PPLM and CTRL control text generation from the higher-level means of BOWs and control codes. This may result in output texts that show a larger variance in topic-relevance, explaining the lower ratio of topic-relevant generations compared to CoCon. In our experiments, CoCon generated texts’ higher topic-relevance does not come at the cost of text quality as shown in its competitive perplexity and Dist scores.