Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners
Table 4: Human evaluation results on the automated and true emails. Scores for Syntax (Syn), Coherency (Coh) and Fluency (Flu) vary between [1, 5]. Detection Rate has been reported as a percentage
['[BOLD] Email Content', '[BOLD] Scores  [BOLD] Syn', '[BOLD] Scores  [BOLD] Coh', '[BOLD] Scores  [BOLD] Flu', '[BOLD] Detection Rate', '¯¯¯ [BOLD] W', '[ITALIC] SDW', '¯¯¯ [BOLD] S', '[ITALIC] SDS']
We report the scores for syntax, coherency and fluency, averaged across all participants, on the combined set of system generated emails - Generatedall. We also report the same on the subset of emails generated by each of the baseline and deep generation models - Generatorword and Generatedsentence respectively, to compare the difference in system performance. We also report the scores on the combined set of system generated emails - Generatedall. the generated content is still behind in terms of coherency and fluency. We also include the mean and standard deviation in the number of words (¯¯¯¯¯¯W, SDW) and sentences (¯¯¯¯S, SDS) in the generated and the manually written emails used in our survey.