As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines.
Zhang and Lapata first propose to generate Chinese poems incrementally with Recurrent Neural Network (RNN), which packs the full context into a single vector by a Convolutional Sentence Model. To enhance coherence, their model needs to be interpolated with two SMT features, as the authors state. Yan generates Chinese quatrains using two RNNs with an iterative polishing schema, which tries to refine the poem generated in one pass for several times. utilize neural Encoder-Decoder with attention mechanism @cite_21 and trains different models to generate lines in different positions of a poem. propose a two-stage Chinese classical poetry generation method which at first plans the sub-keywords of the poem, then generates each line sequentially with the allocated sub-keyword. However, the beforehand extracted planning patterns bring some explicit constraints, which may take a risk of losing some degree of flexibility as discussed in Section 1.
@cite_21
2133564696
Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation. In this paper, we conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and propose to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly. With this new approach, we achieve a translation performance comparable to the existing state-of-the-art phrase-based system on the task of English-to-French translation. Furthermore, qualitative analysis reveals that the (soft-)alignments found by the model agree well with our intuition.