MOCHA: A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective
This work addresses the gap in coherent text generation for applications like storytelling and journalism, though it is incremental as it builds on existing pre-trained models.
The paper tackled the problem of generating coherent narrative texts by proposing a multi-task training strategy based on cognitive writing theory, which improved performance on story generation, news article writing, and argument generation tasks, with human evaluations confirming better coherence.
Teaching neural models to generate narrative coherent texts is a critical problem. Recent pre-trained language models have achieved promising results, but there is still a gap between human written texts and machine-generated outputs. In this work, we propose a novel multi-task training strategy for coherent text generation grounded on the cognitive theory of writing, which empowers the model to learn essential subskills needed for writing including planning and reviewing besides end-to-end generation. We extensively evaluate our model on three open-ended generation tasks including story generation, news article writing and argument generation. Experiments show that our model achieves better results on both few-shot and fully-supervised settings than strong baselines, and human evaluations confirm that our model can generate more coherent outputs.