SORTIE : Dependency-Aware Symbolic Reasoning for Logical Data-to-text Generation (2023)

Abstract: Logical data-to-text generation is a representative task in measuring the capabilities of both language generation and complex reasoning. Despite the introduction of reasoning skills in generation, existing works still rely on neural language models to output the final table description. However, due to the inefficacy of neural language models in complex reasoning, these methods inevitably have difficulty working out key entities in the description and might produce unfaithful descriptions. To alleviate these issues, we propose a dependency-aware symbolic reasoning framework that reasons out each entity in the table description with our designed table-compatible programming language. To figure out the dependency relationship among entities, we devise an entity scheduling mechanism to determine the order of programme synthesis such that the reasoning of an entity only relies on other “resolved” entities. Experiments on three datasets and three backbones show that ours outperforms previous methods not only in surface-level fidelity but also in logical fidelity. Notably, the proposed framework enhances GPT-2, BART and T5 with an absolute improvement of 5.7%~11.5% on SP-Acc.

## introduction generating logical-consistent sentences is an integral part of human intelligence and has attracted broad research interests in the field of natural language processing recently @xcite @xcite . one of the most prominent attempts to investigate this capability in neural models is logical data-to-text generation @xcite , which requires conducting intricate reasoning on † xueliang zhao and tingchen fu contribute equally to this work. this work was done during their internship at tencent ai lab. there are 4 games with the attendance of 16000, the highest attendance in 1992 -93 vancouver canucks season. ## conclusion we propose a neural symbolic approach for logical data-to-text generation. with a table-compatible programming language, our approach automatically synthesizes a programme to reason out each entity. specifically, to handle the inter-dependency between entities, we propose an entity scheduling mechanism that dynamically predicts the reasoning order of entities such that the entity to be reasoned at each iteration has a minimum dependency on "unseen" entities. in addition, to deal with the paucity of human annotations of both pro-grammes and scheduling order, we put forward a weak supervision method and a self-adaptive learning algorithm to mitigate the spurious correlation issue. evaluation results on three benchmarks show that our model can significantly outperform stateof-the-art approaches, and considerably boost the performance of a pre-trained language model in terms of logical fidelity.