A Hierarchical Attention Based Seq2seq Model for Chinese Lyrics Generation
arXiv:1906.06481
Abstract
In this paper, we comprehensively study on context-aware generation of Chinese song lyrics. Conventional text generative models generate a sequence or sentence word by word, failing to consider the contextual relationship between sentences. Taking account into the characteristics of lyrics, a hierarchical attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese lyrics generation. With encoding of word-level and sentence-level contextual information, this model promotes the topic relevance and consistency of generation. A large Chinese lyrics corpus is also leveraged for model training. Eventually, results of automatic and human evaluations demonstrate that our model is able to compose complete Chinese lyrics with one united topic constraint.
accepted by The 16th Pacific Rim International Conference on AI
References in corpus (6)
- Adam: A Method for Stochastic Optimization
- Neural Machine Translation by Jointly Learning to Align and Translate
- Sequence to Sequence Learning with Neural Networks
- Chinese Poetry Generation with Planning based Neural Network
- Context-aware Natural Language Generation with Recurrent Neural Networks
- TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning