Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning (2023)

Abstract: In this paper, we present a novel approach for data-to-text generation that addresses the limitations of current methods that primarily focus on specific types of structured data. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios by providing a unified representation that can handle various forms of structured data such as tables, knowledge graph triples, and meaning representations. We demonstrate that our proposed approach can effectively adapt to new structured forms, and can improve performance in comparison to current methods. For example, our method resulted in a 66% improvement in zero-shot BLEU scores when transferring models trained on table inputs to a knowledge graph dataset. Our proposed method is an important step towards a more general data-to-text generation framework.

## introduction data-to-text generation is the task of converting structured data into natural language text that can be easily understood by humans. previous methods for data-to-text generation have been limited to specific structured forms. for example, graph neural networks (gnns) have been used to encode knowledge graph input @xcite @xcite , while table-specific encoders have been proposed for tables @xcite @xcite @xcite . however, these methods are not easily transferable to other structured forms, creating a barrier for scientific development and preventing models from learning across tasks. recent work has attempted to address the problem of limited structured form applicability by using pretrained language models (plms) as a single text-to-text framework for all data structures, by linearizing the data as text sequences. as shown by @xcite ; @xcite , these methods achieve state-of-the-art performance on a wide range of data-to-text tasks. despite the advancements made in the field, there are still unresolved questions regarding the relationship between various structured forms, particularly in the context of zero-shot or few-shot settings, where models are required to rapidly adapt to new structured forms. this is particularly pertinent in cases of data scarcity, when structured forms vary across different domains and there is a limited amount of data available for a specific structured form, but a single model is needed to operate on all of them. such an example is to adapt a knowledge-graph-to-text model to a new domain with data in table format. even when there is an abundance of data, developing a universal model that can handle all structured forms remains a challenging task. as seen in @xcite , a multi-task trained model may perform worse than a single-task model on table inputs. one important reason for such performance drop is because previous research has not fully examined the impact of various linearization methods on these tasks and their effect on cross-task generalization. despite the use of text-to-text transformers, linearization methods for various structured forms remain diverse, and even within one structured form, linearization can vary across studies. for example, the linearization of kg triples differs in @xcite and @xcite , highlighting the need for further research on the relationship between data formats and data-to-text tasks. in this paper, we address the unresolved questions surrounding the relationship between various structured forms by introducing a unified representation for knowledge graphs, tables, and meaning representations. we demonstrate that our method allows for the conversion of knowledge graph triples and meaning representations into virtual ta-bles, which can then be linearized in a consistent manner. through evaluating our approach on five representative data-to-text tasks across the aforementioned formats, we show that our method not only achieves competitive performance compared to other data-specific linearizations for individual tasks, but also leads to significant improvements in transfer learning scenarios across structured forms, particularly in zero-shot or few-shot settings. for example, using the unified representation improves the zero-shot bleu score by relatively 66% when transferring from totto @xcite to dart @xcite . additionally, our approach results in improved performance when used in multi-task settings compared to models trained with varied linearizations. these results provide a clear indication of the effectiveness of our proposed unified representation in enhancing cross-task generalization. structured data linearization recent developments in pretrained language models @xcite @xcite have made it possible to use a single text-to-text framework for various types of data by linearizing them as text sequences. studies have been conducted on finetuning plms on table input @xcite and knowledge graph input @xcite , single-task and multi-task training on a collection of structured data grounding tasks @xcite , and the effectiveness of pretraining and fine-tuning strategies for data-to-text tasks @xcite and table-based question answering tasks @xcite . these studies have consistently found that linearizing structured data as a sequence of tokens without modifying the model structure, is a simple yet effective strategy that outperforms pipelined neural architectures specifically tailored to particular data types. zero/few-shot data-to-text generation the studies such as @xcite and @xcite have evaluated the zero and few-shot performance of plms on knowledge graph input, highlighting the benefits of a joint pretraining strategy on knowledge graphs and texts for learning better kg representations. @xcite studied the prompt-tuning method for kg-to-text generation and found it to be effective in a fewshot setting. @xcite combines plm with a table content selector using a switch policy. other researchers have also explored methods such as data augmentation @xcite and retrieval-based input augmentation @xcite to aid in few-shot data-to-text generation. @xcite proposes a pipeline approach involving a sequence of operations, such as ordering and aggregation, and only finetunes the plms of these modules to make the pipeline more domainindependent. ## unified representation in this section, we demonstrate that structured data, such as tables, highlighted cells, knowledge graph triples, and meaning representations, can be linearized in a consistent manner. we begin by show-figure 1 : unified representation of three data types: table, kg triples, and meaning representations. the latter two are first converted to virtual tables, and then linearized using the same method as table input. ## conclusion and future work we have introduced a unified representation approach for data-to-text tasks, which effectively converts table contents, knowledge graph triples, and meaning representations into a single representa-tion. our experiments demonstrate that this unified representation significantly improves generalization across different structured forms, especially in zero-shot or few-shot settings. our method is particularly beneficial in situations where data is scarce. additionally, by using the unified representation, our multi-task-trained models consistently outperform single-task models, which is in contrast to previous findings that mixing different data types can negatively impact overall performance. one future direction is to apply our method to other tasks that involve heterogeneous inputs, such as question answering over knowledge bases, where knowledge can be stored in both tables and knowledge graphs. it would also be interesting to investigate whether a model pre-trained on large knowledge graphs can more effectively transfer learned commonsense knowledge to table qa tasks, when using our unified representation approach.