MOCHA : A Multi-Task Training Approach for Coherent Text Generation from Cognitive Perspective (2022)

Abstract: 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 long 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.

## introduction with the recent development of pretraining techniques, large neural language models have achieved impressive results on various text generation tasks and can generate fluent outputs. however, when generating long-form texts (i.e., paragraphs with multiple sentences), there is still a large gap between machine-generated outputs and human written texts: the generated outputs usually suffer from incoherence issues and fail to maintain overall narrative coherence @xcite . one possible reason for the above defects is the lack of effective text planning as global guidance to control the generation process. compared with the traditional generation systems which often decompose the generation task into text planning and surface realization @xcite , current autoregressive neural language models are typically trained to produce texts in a left-to-right token-level manner, which lacks anchored goal to constrain the generation process @xcite . recent studies incorporate text planning into neural models by leveraging structured representations @xcite or latent variables @xcite as high-level plans, but they need manually designed domain-specific plans or complicated supervision signals to train the model. another reason is the ineffective usage of the negative samples as contrasts to teach the model better distinguish between correct and incorrect targets. negative samples are useful to enhance the model ability to generate better outputs @xcite . recent work explores techinques such as contrastive learning @xcite @xcite and unlikelihood training @xcite to leverage negative samples for model training. we draw our motivations from the cognitive process theory of writing @xcite : "writing is best understood as a set of distinct thinking processes which writers orchestrate or organize during the act of composing". in particular, the basic mental process of writing includes planning, translating (surface realization), and reviewing, where the reviewing process further involves evaluating and revising subskills. current language models are typically trained to maximize the token-level loglikelihood and thus learn to acquire the writing skills all at once. however, as stated by @xcite , learning the whole set of task components for writing at once makes the learning process very hard, and they suggest that the intermediate tasks benefit to acquiring and exercising different writing subskills. in this work, we propose mocha, a multi-task training approach for coherent text generation by enriching the token-level generation objective with additional tasks specifically designed for different writing subskills grounded on the cognitive perspective. specifically, we introduce two additional generate a coherent output. [title] "objectivism" is the most optimal way to go through life… output: imagine that there are two groups of equal size. group a follows your philosophy. group b identifies with their entire group to some degree, and will make some level of effort to help its members, even if it does not immediately benefit themselves. which group will be selected for? it's a complex question … produce a plan. [title] "objectivism" is the most optimal way to go through life… output: <@xmath0; equal @xmath1; @xmath2; entire group; will make some level; effort; @xmath3; many selective pressures… conduct surface realization. @xcite : "objectivism" is the most optimal... our work is closely related to the recent multitask training approach @xcite by converting different tasks into text-to-text transfer with corresponding prompts. recent work @xcite has shown that multi-task learning (mtl) with shared parameters across different tasks can effectively improve the model performance on text understanding @xcite , dialogue generation @xcite , and structured knowledge grounding @xcite . different from previous work, we study coherent long text generation with mtl to tackle different subskills needed for writing. experimental results show that our method outperforms strong baselines and achieves better few-shot performance compared with vanilla t5 on story generation, counter-argument generation and news article writing. human evaluation further confirms that our method can generate more coherent outputs. data and code are available at: https: //github.com/derekkk/mocha-emnlp22 ## method text generation is typically formulated as a sequence-to-sequence (seq2seq) transformation: p(y|x) = n @xmath4:t-1 , x), where (x, y) is a source-target pair. we adopt the state-of-the-art model t5 @xcite as the backbone, which is an encoder-decoder transformer. for each sample, we introduce additional training objectives to jointly improve the writing ability. our training objectives include end-to-end generation, decomposed generation and reviewing tasks. all tasks are converted to text-to-text transfer with a task prompt prepended to the source input. notably, training samples of the augmented tasks can be constructed automatically, without further data labeling efforts. the overall framework is illustrated in figure 1 . ## conclusion in this work, we present a multitask training approach driven by cognitive theory of writing to improve the model ability on coherent long text generation. we introduce decomposed generations tasks and reviewing tasks with different prompts to tackle essential subskills needed for generating coherent outputs. our model achieves better results on three long text generation tasks under both full training and few-shot settings, and can generate better and more coherent outputs.