Chinese Poetry Generation with a Salient-Clue Mechanism
Table 2: Human evaluation results. Diacritics * (p<0.05) and ** (p<0.01) indicates SC models significantly outperform the three baselines; + (p<0.05) and ++ (p<0.01) indicates Human is significantly better than all the five models. The Intraclass Correlation Coefficient of the four groups of scores is 0.596, which indicates an acceptable inter-annotator agreement.
['[BOLD] Models', '[BOLD] Fluency', '[BOLD] Fluency', '[BOLD] Coherence', '[BOLD] Coherence', '[BOLD] Meaningfulness', '[BOLD] Meaningfulness', '[BOLD] Poeticness', '[BOLD] Poeticness', '[BOLD] Entirety', '[BOLD] Entirety']
SC and Style-SC achieve better results than other models and get close to Human, though there is still a gap. Especially on Coherence, our Style-SC gets the same score as Human for Wujue. Moreover, Style-SC makes a considerable improvement on Coherence compared to SC (+0.05 for Wujue and +0.15 for Qijue), which demonstrates that consistent style can actually enhance the coherence, though it’s not easy to predict an appropriate style automatically. Interestingly, Style-SC outperforms SC on most criteria, except for Poeticness. We believe this is mainly because that style control forces the model to always generate some style-related words, which limits the imagination and thus hurts the Poeticness.