Neural Poetry: Learning to Generate Poems using SyllablesThis is a post-peer-review, pre-copyedit version of an article published in LNCS, volume 11730. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-30490-4_26
Table 3: Experts evaluations restricted to tercets generated by sy-LM. Votes vary from 0 to 5. The average rate is also reported. For comparisons, in the last line we also report the average rate in the case of Dante’s real tercets (Poet).
['[EMPTY]', '[BOLD] Readability', '[BOLD] Emotion', '[BOLD] Meter', '[BOLD] Rhyme', '[BOLD] Style']
In another experiment, we involved 4 expert judges with academic experiences on Dante Alighieri’s production. Each expert evaluated 20 tercets, scoring (from 0 to 5) different properties of each of them: emotion, meter, rhyme, readability and adherence to the author’s style. In particular, 10 tercets were generated by sy-LM and 10 were extracted from the Divine Comedy. Judges were not aware of how tercets were distributed. Dante’s tercets are better scored, of course, however, we observe a good evaluation of the quality of the rhymes produced by sy-LM. Considering that judges know very well Dante Alighieri, it is interesting to see that they are experiencing some of the author ’s style in the generated tercets. Finally, judges applied very strict criteria in evaluating the meter, giving low scores whenever a small incoherence with Dante’s meter was apparently detected, even if they reported that it was not far from the ideal case.