(Male, Bachelor) and (Female, Ph.D) have different connotations: Parallelly Annotated Stylistic Language Dataset with Multiple Personas
Table 5: Supervised style transfer. GloVe initializes with pre-trained word embeddings. PreTr. denotes pre-training on YAFC. Hard measures are BLEU2, METEOR, and ROUGE, and soft measures are EmbedingAveraging and VectorExtrema.
['Models: ( [ITALIC] S,  [ITALIC] α) → ^ [ITALIC] S', 'Hard (^ [ITALIC] S, [BOLD] ¯ [ITALIC] Sα)  [BOLD] B2', 'Hard (^ [ITALIC] S, [BOLD] ¯ [ITALIC] Sα)  [BOLD] M', 'Hard (^ [ITALIC] S, [BOLD] ¯ [ITALIC] Sα)  [BOLD] R', 'Soft (^ [ITALIC] S, [BOLD] ¯ [ITALIC] Sα)  [BOLD] EA', 'Soft (^ [ITALIC] S, [BOLD] ¯ [ITALIC] Sα)  [BOLD] VE']
We observe that initializing both en/decoder’s word embeddings with GloVe pennington2014glove improves model performance on most metrics. Pretraining (PreTr.) on the formality style transfer data YAFC rao2018dear further helps performance. All supervised S2S approaches outperform both retrieval-based baselines on all measures. This illustrates that the performance scores achieved are not simply a result of memorizing the training set. S2S methods surpass AsItIs on both soft measures and ROUGE. The significant gap that remains on BLEU remains a point of exploration for future work. The significant improvement against the unsupervised methods shen2017style; lample2017unsupervised indicates the usefulness of the parallel text in PASTEL.