Learning Sentiment Memories for Sentiment Modification without Parallel Data
Table 2: Results of human evaluation.
['Model', 'Sentiment', 'Content', 'Fluency']
We also involve human evaluation to measure the quality of generated text. Each item contains an input and three outputs generated by different systems. Then 200 items are distributed to 2 annotators with linguistic background. The annotators have no idea about which system the output is from. They are asked to score the output on three criteria on a scale from 1 to 10: the transformed sentiment degree, the content preservation degree, and the fluency. Our model has obvious advantage over the baseline systems in content preservation, and also performs well in other aspects.