This paper introduces a novel training model, self-training from self-memory (STSM) in data-to-text generation (DTG), allowing the model to self-train on subsets, including self-memory as outputs inferred directly from the trained models and/or the new data. The quality of self-memory is validated by two models, data-to-text (D2T) and text-to-data (T2D), by two pre-defined conditions: (1) the appearance of all source values in the outputs of the D2T model and (2) the ability to convert back to source data in the outputs in the T2D model. We utilize a greedy algorithm to generate shorter D2T outputs if they contain all source values. Subsequently, we use the T2D model to confirm that these outputs can capture input relationships by demonstrating their capacity to convert text back into data. With 30% of the dataset, we can train the D2T model with a competitive performance compared to full training in the same setup. We experiment with our model on two datasets, E2E NLG and DART. STSM offers the D2T model a generalization capability from its subset memory while reducing training data volume. Ultimately, we anticipate that this paper will contribute to continual learning solutions that adapt to new training data, incorporating it as a form of self-memory in DTG tasks. The curated dataset is publicly available at: https://github.com/hoangthangta/STSM.

翻译：本文提出一种新颖的训练模型——数据到文本生成中的自我记忆自训练方法（STSM），允许模型在子集上进行自训练，其中子集包含直接从已训练模型和/或新数据推断的输出（即自我记忆）。通过数据到文本（D2T）和文本到数据（T2D）两个模型，依据两项预设条件验证自我记忆质量：（1）D2T模型输出中包含所有源数据值；（2）T2D模型输出具备将文本还原为源数据的能力。我们采用贪心算法生成包含所有源数据值的更短D2T输出，进而利用T2D模型通过验证输出恢复输入关系的能力（即文本转回数据），确认这些输出能捕捉输入关系。仅使用30%数据集训练D2T模型，即可达到与全量数据训练相当的竞争性性能。我们在E2E NLG和DART两个数据集上进行实验，结果表明STSM使D2T模型能够从子集记忆中获取泛化能力，同时降低训练数据规模。最终，我们期望本文能为持续学习方案提供贡献，使模型能够将新训练数据作为自我记忆融入DTG任务。整理后的数据集公开于：https://github.com/hoangthangta/STSM