Controlling Linguistic Style Aspects in Neural Language Generation
This work addresses the need for more nuanced text generation in natural language processing, though it is incremental as it builds on existing conditioned RNN models.
The paper tackled the problem of controlling both content and stylistic aspects in neural language generation, demonstrating success in generating coherent sentences with specified style and content in the movie reviews domain.
Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts. We demonstrate the approach on the movie reviews domain and show that it is successful in generating coherent sentences corresponding to the required linguistic style and content.