Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning (2022)

Abstract: Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations. Accordingly, we propose an equivariance learning framework, which encodes tables with a structure-aware self-attention mechanism. This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective. Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells. Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformation-based data augmentation, have seen significant performance drops.

## introduction table-to-text generation seeks to generate natural language descriptions for content and entailed conclusions in tables. it is an important task that not only makes ubiquitous tabular data more discoverable and accessible, but also supports downstream tasks of tabular semantic retrieval @xcite , reasoning @xcite , fact checking @xcite and table-assisted question answering @xcite . while rich and diverse facts can be presented in a table, the controlled table-to-text generation task, which generates focused textual descriptions for highlighted subparts of a table, has garnered much attention recently @xcite @xcite . prior studies on controlled table-to-text generation often employ a sequence-to-sequence generation method, which merely captures the table as a linear structure @xcite @xcite . however, table layouts, though overlooked by prior studies, are key to the generation from two perspectives. first, table layouts indicate the relations among cells that collectively present a fact, which are however not simply captured by a linearized table. for example, if we linearize the first table row-wise in fig. 1 , wai siu-bo will be next to both royal tramp and king of beggers, so that it is not clear this role belongs to which film. second, the same content can be equivalently expressed in tables with different layouts. while linearization simplifies the layout representation, it causes brittle generation when table layouts change. fig. 1 shows two tables with the same content but different layouts, for which the generations by t5 are largely inconsistent. in this paper, we focus on improving controlled table-to-text generation systems by incorporating two properties: structure-awareness and transformation-invariance. structure-awareness, which seeks to understand cell relations indicated by the table structure, is essential for capturing contextualized cell information. transformationinvariance, which seeks to make the model insensitive to content-invariant structural transformations (including transpose, row shuffle and column shuffle), is essential for model robustness. however, incorporating structure-awareness and transformation-invariance into existing generative neural networks is nontrivial, especially when preserving the generation ability of pretrained models as much as possible. we enforce the awareness of table layouts and robustness to content-invariant structural transformations on pretrained generative models with an equivariance learning framework, namely layout aware and transformation invariant controlled table -to-text generation (lattice ). lattice encodes tables with a transformation-invariant graph masking technology. this prunes the full self-attention structure into an order-invariant graph-based attention that captures the connected graph of cells belonging to the same row or column, and differentiates between relevant cells and irrelevant cells from the structural perspective. lattice also modifies the positional encoding mechanism to preserve the relative position of tokens within the same cell but enforces position invariance among different cells. our technology is free to be plugged into existing table-to-text generation models, and has improved t5-based models @xcite on @xcite and hitab @xcite . moreover, on a harder version of totto, we preserve promising performance, while previous sota systems, even with transformation-based data augmentation, have seen significant performance drops. our contributions are three-fold. first, we propose two essential properties of a precise and robust controlled table-to-text generation system, i.e. structure-awareness and transformation-invariance. second, we demonstrate how our transformationinvariant graph masking technology can enforce these two properties, and effectively enhance a representative group of transformer-based generative models, i.e. t5-based models, for more generalizable and accurate generation. third, in addition to experiments on totto and hitab benchmarks, we evaluate our model on a harder version of totto with a special focus on robustness to content-invariant structural transformations. ## method in this section, we first describe the preliminaries of content-invariant table transformations, base models and the input format for controlled table-to-text generation ( §2.1). then we introduce the technical details about how the transformation-invariant graph masking technology in lattice enforces the model to be structure-aware and transformationinvariant ( §2.2). finally, we present two alternative techniques for strengthening the transformationinvariance to be compared with lattice ( §2.3). ## experiments in this section, we conduct experiments on two benchmark datasets. first, we introduce the details of datasets, baselines, evaluation metrics and our implementation ( §3.1). then, we show the overall performance of lattice ( §3.2). after that, we analyze the model robustness on a harder version of the totto dataset where content-invariant perturbations are introduced ( §3.3). finally, we provide ablation study on components of transformationinvariant graph masking ( §3.4). ## related work we review two relevant research topics. since both topics have a large body of work, we provide a selected summary. table-to-text generation. table-to-text generation seeks to generate textual descriptions for tabular data. in comparison to text-to-text generation, the input of table-to-text generation is semistructured data. early studies adapt the encoderdecoder framework to data-to-text generation with encoders aggregating cell information @xcite @xcite . followed by the success of massively pre-trained sequence-to-sequence transformer models @xcite , recent sota systems apply these models to a few prior studies introduce structural information to improve model performance on tableto-text generation, either by incorporating token position @xcite , or by aggregating row and column level information @xcite @xcite . however, none of existing methods can be directly applied to pretrained transformer-based generative models, especially when we want to ensure model robustness to content-invariant table transformations. our method enforces both structure-awareness and transformation-invariance to such models. ## conclusion we propose lattice, a structure-aware equivariance learning framework for controlled tableto-text generation. our experimental results verify the importance of structure-awareness and transformation-invariance, two key properties enforced in lattice, towards precise and robust description generation for tabular content. the proposed properties and equivariance learning framework aligns well with the nature of information organized in tables. future research can consider extending the structure-aware equivariance learning framework to other data-to-text generation tasks @xcite , tabular reasoning or retrieval tasks @xcite @xcite , and pretraining representation on textual and tabular data @xcite @xcite .