Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework
Table 1: Sketch-Fill-A-R achieves significantly lower out-of-sample perplexity than KVMemNet. Perplexity for Sketch-Fill-A-R is measured over the sketch template responses. The number of persona tag occurrences is very small, constituting 8% of the total words. See Appendix for more information.
['[BOLD] Model', '[BOLD] Parameters', '[BOLD] Perplexity']
All Sketch-Fill-A-R models use language model reranking All input tokens xct,xpt were first encoded using 300-dimensional GLoVe word embeddings e(xt) Pennington et al. . All models were trained by minimizing loss on the ground truth sketch response y∗0: U: minθ−U∑u=0⟨y∗u,logP(yu|x0: T,y0:u−1;θ)⟩. (12) For training details, see the Appendix. Sketch-Fill models outperform KVMemNet on validation perplexity, while using significantly fewer weights than KVMemNet. This suggests the structure of Sketch-Fill models fits well with chit-chat dialogue.