Chinese Song Iambics Generation with Neural Attention-based Model
Table 3: Averaged ratings for Song iambics generation with different methods and settings. ‘Partridge Sky’ and ‘Pusaman’ are the two tunes in the evaluation.
['Model', 'Poeticness Partridge Sky', 'Poeticness Pusaman', 'Fluency Partridge Sky', 'Fluency Pusaman', 'Meaningfulness Partridge Sky', 'Meaningfulness Pusaman', 'Average']
It can be seen that our model outperforms both the SMT-based and the RNNLM-based approaches that are often used in Chinese poem Generation task. Note that in this experiment, the RNNLM-based approach is also based on LSTM, and so holds the same advantage as the attention model in learning long-distance patterns. However the RNN model tends to swift from the initial theme, hence causing inconsistency during the generation.