Abstract
Expressing diverse sentiments is one of the main
purposes of human poetry creation. Existing Chinese poetry generation models have made great
progress in poetry quality, but they all neglected
to endow generated poems with specific sentiments. Such defect leads to strong sentiment collapse or bias and thus hurts the diversity and semantics of generated poems. Meanwhile, there
are few sentimental Chinese poetry resources for
studying. To address this problem, we first collect a manually-labelled sentimental poetry corpus with fine-grained sentiment labels. Then
we propose a novel semi-supervised conditional
Variational Auto-Encoder model for sentimentcontrollable poetry generation. Besides, since poetry is discourse-level text where the polarity and
intensity of sentiment could transfer among lines,
we incorporate a temporal module to capture sentiment transition patterns among different lines. Experimental results show our model can control the
sentiment of not only a whole poem but also each
line, and improve the poetry diversity against the
state-of-the-art models without losing quality