Generating open-domain conversational responses in the desired style usually suffers from the lack of parallel data in the style . Meanwhile , using monolingual stylistic data to increase style intensity often leads to the expense of decreasing content relevance . In this paper , we propose to disentangle the content and style in latent space by diluting sentence-level information in style representations . Combining the desired style representation and a response content representation will then obtain a stylistic response . Our approach achieves a higher BERT-based style intensity score and comparable BLEU scores , compared with baselines . Human evaluation results show that our approach significantly improves style intensity and maintains content relevance . Linguistic style is an essential aspect of natural language interaction and provides particular ways of using language to engage with the audiences ( Kabbara and Cheung , 2016 ) . In human-bot conversations , it is crucial to generate stylistic responses for increasing user engagement to conversational systems ( Gan et al . , 2017 ) . Currently , most of the existing parallel datasets are not stylistically consistent . Samples in these datasets are usually contributed by a variety of users , resulting in an averaging effect across style characteristics ( Zhang et al . , 2018a ) . Meanwhile , constructing a parallel stylistic dataset for training the open-domain conversational agents is both labor-intensive and time-consuming . Recent studies show the effect of stylizing responses using a monolingual dataset in the desired style and a conventional conversational dataset ( Niu and Bansal , 2018 ; Gao et al . , 2019b ) . However , increasing style intensity often leads to ( Niu and Bansal , 2018 ) , Style Fusion ( Gao et al . , 2019b ) , and our approach , targeting the Holmes style , which is quite formal and polite . the expense of decreasing content relevance between dialogue history and response . As an example in Figure 1 shows , Niu and Bansal ( 2018 ) independently train a response generation model and a stylistic language model and subsequently interpolates them in the inference phase . Lacking the interaction between the stylistic language model and response generation encoder , it usually yields a trade-off between style intensity and content relevance . Gao et al . ( 2019a , b ) fuse a structured latent space where the direction denotes the diversity , and the distance denotes style intensity and content relevance . The main issue is that style intensity and content relevance are contradictory in measurement but are coupling to the same " distance " metric of the latent space . To sum up , the key issue of the above studies is the improper entanglement of style and content . To address the issue , we propose to disentangle the style and content of a response . The disentanglement is conducted on the structured latent space , where each sentence ( dialogue history , response , and stylistic sentence ) is projected into a vector representation . We further split the representation into two components : style and content representations . The former is a corpus-level feature since sentences within a dataset have the same style . In contrast , the content representation is a sentence-level feature decided by a sentence itself . We thus disentangle the content and style by diluting sentence-level information in the style representation . This encourages the encoding of content information into the content representation . Otherwise , the content information will be corrupted in the style representation , making it hard to reconstruct the original content in the subsequent decoding process . We conduct experiments on DailyDialogue conversational dataset ( Li et al . , 2017 ) and Holmes monolingual stylistic dataset ( Gao et al . , 2019b ) . Experimental results show that our proposed approach improves style intensity and maintains content relevance . Our contributions are listed below : • We propose a unified framework to simultaneously improve style intensity and maintain content relevance for neural stylistic response generation . • We introduce a scheme of learning latent variables by a diluting strategy to disentangle the style and content . • Experimental results show that our approach achieves higher performance in style intensity without decreasing content relevance , compared with previous approaches . We propose a uniform framework to simultaneously improve the style intensity and maintain the content relevance for neural stylistic response generation . In contrast to existing approaches , our approach disentangles the style and the content in the latent space by a diluting strategy . Experiments show that our approach improves the style intensity of generated responses and maintains the content relevance at the same time , which demonstrates the effectiveness of this approach .

Challenge: Because existing studies for open-domain conversational response generation have improper entanglement of style and content, they fail to achieve the desired styles for content relevance.
Approach: They propose to disentangle the content and style in latent space by diluting sentence-level information, then combine style and content representations to produce stylistic responses.
Outcome: The proposed method achieves a higher BERT-based style intensity score and comparable BLEU scores, and human evaluation shows improvements in style intensity and content relevance.