# Quality Control for Distantly-Supervised Data-to-Text Generation via Meta Learning
###### Abstract  
Data-to-text generation plays an important role in natural language processing by processing structured data and helping people understand those data by generating user-friendly descriptive text. It can be applied to news generation, financial report generation, customer service, etc. However, in practice, it needs to adapt to different domains that may lack an annotated training corpus. To alleviate this dataset scarcity problem, distantly-supervised data-to-text generation has emerged, which constructs a training corpus automatically and is more practical to apply to new domains when well-aligned data is expensive to obtain. However, this distant supervision method of training induces an over-generation problem since the automatically aligned text includes hallucination. These expressions cannot be inferred from the data, misguiding the model to produce unfaithful text. To exploit the noisy dataset while maintaining faithfulness, we empower the neural data-to-text model by dynamically increasing the weights of those well-aligned training instances and reducing the weights of the low-quality ones via meta learning. To our best knowledge, we are the first to alleviate the noise in distantly-supervised data-to-text generation via meta learning. In addition, we rewrite those low-quality texts to provide better training instances. Finally, we construct a new distantly-supervised dataset, DIST-ToToTo (abbreviation for Distantly-supervised Table-To-Text), and conduct experiments on both the benchmark WITA (abbreviation for the data source Wikipedia and Wikidata) and DIST-ToTo datasets. The evaluation results show that our model can improve the state-of-the-art DSG (abbreviation for Distant Supervision Generation) model across all automatic evaluation metrics, with an improvement of 3.72% on the WITA dataset and 3.82% on the DIST-ToTo dataset in terms of the widely used metric BLEU (abbreviation for BiLingual Evaluation Understudy). Furthermore, based on human evaluation, our model can generate more grammatically correct and more faithful text compared to the state-of-the-art DSG model. Keywords:data-to-text generation; Natural Language Generation; natural language processing; deep learning; meta learning; Artificial Intelligence +
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confined to specific domains or data sources as it is difficult and expensive to construct these datasets, thus, making it difficult to quickly adapt to different domains. Traditionally, the data-to-text generation task has two major types of methods: pipeline-style systems [9,10] and end-to-end models [2,3,11]. The former breaks the generation process into multiple stages that decide what information in the data to describe and then to transform that information into text. In recent years, with the help of high-quality datasets [2,3] and the success of deep learning, end-to-end models [12, 13, 14], which take the structured data as input and directly generate the text through neural language models, have become mainstream. Many researchers have explored improving end-to-end models from different angles. Some researchers have focused on improving the content selection ability [3,12] so that the model can generate more informative text. Some researchers explored enhancing the end-to-end model's ability to generate more faithful text [13,14], that is, the information in the text is more consistent with the data. To increase the informativeness of the text, rather than simply describe the information in the data, some researchers [15, 16, 17, 18, 19] explored incorporating reasoning ability into the end-to-end model so that they could provide more insights about the data. Furthermore, some researchers explored few-shot data-to-text generation [20, 21, 22, 23], which trains the end-to-end model with a limited training corpus. However, the studies above require well-aligned training datasets in order to train the data-driven end-to-end model. However, in practice, the model needs to adapt to different domains that may lack an annotated training corpus.