A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023)

Abstract: Though big progress in table-to-text works, effectively leveraging table structure signals, e.g., hierarchical structure, remains challenging. Besides, deliberating generated descriptions proves to be effective for table-to-text. However, determining the appropriate outcome when encountering multi-pass candidates is another challenge. To this end, we propose a novel table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention, namely SG-HMA. Specifically, we formulate the table structure into a multidominance (MD) structure and devise a heterogenous multidominance attention (HMA) to comprehensively explore the complex interactions encoded in the hierarchical structure, which can further deliver rich signals for text generation with the help of pre-trained language models (PLMs). Afterward, a contrastive loss is introduced to align the generation objective with evaluation metrics, so the more faithful generated descriptions can be guaranteed. We conduct extensive experiments on three public datasets, demonstrating that SG-HMA outperforms several SOTA methods quantitatively and qualitatively.

## introduction table-to-text, referring to the task of producing a textual description taking the table as input, has been widely applied in different domains, such as weather forecast @xcite , logical tabular reasoning @xcite , and financial report generation @xcite . automatic description generation may shed light on mitigating the time-consuming procedure in table-to-text tasks with bare hands. recent advances in pre-trained language models (plms) have demonstrated significant progress in natural language generation (nlg) @xcite @xcite . to effectively leverage the power of plm, several table-to-text works @xcite serialized the table input via manually defined templates. besides, to preserve the table's structural information, tablegpt @xcite devises a table structure reconstruction task, and tasd @xcite proposes to learn the structure representation explicitly. however, to group the data into categories, the cells in a table (e.g., a pivot table) are often organized in a nested/hierarchical manner using headings and subheadings. the attention map computed by these approaches may fail to harness the perplexing hierarchical table structures. on the other hand, one can effectively deliberate the generated texts from a global perspective with the multi-pass generation paradigm @xcite . while it is hard to terminate the multi-pass generation procedure and determine the appropriate outcome towards faithful descriptions of tables. existing works evaluate and finalize the multi-pass generated texts with the help of reinforcement learning (rl) @xcite or a customized rewrite-evaluator architecture @xcite . however, extra workload might be brought in due to the intractability of rl or a separate evaluation module. to this end, in this paper, we propose a plmbased table-to-text approach with the help of self-evaluated multi-pass generation and heterogeneous multidominance attention (sg-hma). specifically, we first formulate an input table into a multidominance (md) structure @xcite . in this way, the hierarchical relation of the table content can be preserved since one child node in the md structure can have more than one parental node. then, we devise a heterogenous multidominance attention (hma) mechanism to represent the table content with the awareness of complex hierarchical structure. afterward, we deliberate the generated texts with a multi-pass paradigm and develop a contrastive loss to equip the model to generate more faithful table descriptions with self-evaluation. the contributions of this work can be summarized as follows: ## methodology in this section, we introduce the proposed framework in detail. firstly, as shown in fig. 1 , following the table hierarchical structure, we resolve the table into a md structure and apply a hma mechanism to propagate and aggregate the information in the nodes as the table representation, guiding the generation of plm. during the multi-pass generation process, we apply the constrastive learning and table representation to achieve self-evaluation. finally, we can get a well trained se-han to generate ideal table description with tables as input. 4.1 tabular input representation table structure formulation. tables exhibit a natural hierarchical structure, which is comprised of the table components and their complex dependencies. in order to take full advantage of the table structural information, we resolve the hierarchical table structure into the multidominance structure (gračanin-yuksek, 2013) where at least one node has more than one parent node. formally, for each table t, the table hierarchical structure is denoted as the multidominance @xmath0, e} where v is the set of table component nodes extracted from t, and e is the set of directed edges that suggest connectivity among the nodes foot_0 . based on the heterogeneity of the component nodes, v can be further divided into several disjoint subsets following: and v @xmath1, row, column, and content nodes, respectively. furthermore, we use h(r i ) and h(c i ) to signify the located level of row r @xmath2, respectively. we also denote the located row and column in the max level of the content e @xmath3, respectively. based on the heterogeneous node sets, the set of edges e is defined according to the hierarchical structure as: ## conclusion in this article, we introduced sg-hma, a novel approach that can effectively learn table representation and generate accurate summaries. to be specific, sg-hma firstly resolves the table hierarchical structure into a md structure and utilizes the hma to acquire the table representation, which can guide the plm in generating and evaluating text. then, the candidate texts and generation results in each pass are rewrited by sg-hma to create samples for metric ranking with a contrastive objective and obtain more accurate summaries. selfevaluation enables the model have capability to determine when to terminate training process based on the evaluation results, facilitating the generation of high-quality text. finally, extensive experiments conducted on three datasets demonstrate the effectiveness of sg-hma.