PersuAIDE ! An Adaptive Persuasive Text Generation System for Fashion Domain

Persuasiveness is a creative art which aims at inducing certain set of beliefs in the target audience. In an e-commerce seŠing, for a newly launched product, persuasive descriptions are o‰en composed to motivate an online buyer towards a successful purchase. Such descriptions can be catchy taglines, product-summaries, style-tips etc.. In this paper, we present PersuAIDE! a persuasive system based on linguistic creativity to generate various forms of persuasive sentences from the input product speci€cation. To demonstrate the e‚ectiveness of the proposed system, we have applied the technology to fashion domain, where, for a given fashion product like ”red collar shirt” we were able to generate descriptive sentences that not only explain the item but also garner positive aŠention, making it persuasive. PersuAIDE! identi€es fashion related keywords from input speci€cations and intelligently expands the keywords to creative phrases. Once such compatible phrases are obtained, persuasive descriptions are synthesized from the set of phrases and input keywords with the help of a neural language model trained on a large domainspeci€c fashion corpus. We evaluate the system on a large fashion corpus collected from di‚erent sources using (a) automatic text generation metrics used for Machine Translation and Automatic Summarization evaluation and Readability measurement, and (b) human judgment scores evaluating the persuasiveness and ƒuency of the generated text. Experimental results and qualitative analysis show that an unsupervised system like ours can produce more creative and beŠer constructed persuasive output than supervised generative counterparts based on neural sequence-to-sequence models and statistical machine translation.