PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation
This addresses coherence issues in long-form text generation for applications requiring structured content, though it is incremental as it builds on existing Transformer methods.
The authors tackled the problem of incoherence in long-form text generation by proposing PLANET, a framework that uses autoregressive self-attention for dynamic content planning, resulting in significantly outperforming baselines in tasks like counterargument and opinion article generation.
Despite recent progress of pre-trained language models on generating fluent text, existing methods still suffer from incoherence problems in long-form text generation tasks that require proper content control and planning to form a coherent high-level logical flow. In this work, we propose PLANET, a novel generation framework leveraging autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. To guide the generation of output sentences, our framework enriches the Transformer decoder with latent representations to maintain sentence-level semantic plans grounded by bag-of-words. Moreover, we introduce a new coherence-based contrastive learning objective to further improve the coherence of output. Extensive experiments are conducted on two challenging long-form text generation tasks including counterargument generation and opinion article generation. Both automatic and human evaluations show that our method significantly outperforms strong baselines and generates more coherent texts with richer contents.