Adversarially Regularized Autoencoders
Table 3: Sentiment transfer. (Top) Automatic metrics (Transfer/BLEU/Forward PPL/Reverse PPL), (Bottom) Human evaluation metrics (Transfer/Similarity/Naturalness). Cross-Aligned AE is from Shen et al. (2017)
['Model', 'Automatic Evaluation Transfer', 'Automatic Evaluation BLEU', 'Automatic Evaluation Forward', 'Automatic Evaluation Reverse']
We use four automatic metrics: ( i) Transfer: (ii) BLEU: the consistency between the transferred text and the original; (iii) Forward PPL: the fluency of the generated text; (iv) Reverse PPL: measuring the extent to which the generations are representative of the underlying data distribution. Both perplexity numbers are obtained by training an RNN language model. We randomly select 1000 sentences (500/500 positive/negative), obtain the corresponding transfers from both models, and ask crowdworkers to evaluate the sentiment (Positive/Neutral/Negative) and naturalness (1-5, 5 being most natural) of the transferred sentences. We create a separate task in which we show the original and the transferred sentences, and ask them to evaluate the similarity based on sentence structure (1-5, 5 being most similar). We explicitly requested that the reader disregard sentiment in similarity assessment.