Logical Natural Language Generation from Open-Domain Tables

Recent advances in neural language models like BERT and GPT-2 have dramatically improved fluency and coherence in text generation. Prior work such as TabFact and ROTOWIRE focused on fact verification and information extraction, but lacked emphasis on logical inference capabilities. As AI applications demand more reasoning, integrating symbolic inference with neural models has become a key research direction. Existing datasets and models primarily address surface facts, leaving a gap in logical reasoning over complex, open-domain tables. This motivates the development of models that can understand, infer, and generate logically consistent statements, which is essential for trustworthy AI, knowledge reasoning, and explainability.

Traditional table-to-text models generate sequences based solely on token dependencies, ignoring the logical order of facts. This mismatch hampers the model’s ability to produce logically consistent outputs, especially in open-domain settings with diverse inference types. Existing evaluation metrics are inadequate for measuring logical fidelity, often relying on surface similarity. The core challenge is designing models that can plan ahead, understand logical dependencies, and generate statements that are both fluent and entailed by the input data, addressing the gap between sequence order and logical order.

The paper introduces a non-monotonic coarse-to-fine generation framework that separates logical structure planning from surface realization, inspired by semantic parsing techniques. It proposes new automatic metrics (SP-Acc, NLI-Acc) to quantify logical fidelity, moving beyond surface similarity. The approach combines multiple training strategies—reinforcement learning, adversarial regularization, and maximum likelihood—to optimize for logical consistency. Additionally, it constructs a diversified open-domain dataset, LOGICNLG, with rich logical inference types, enabling robust evaluation and training. These innovations collectively push the boundary of logical reasoning in neural text generation.

• �� Define a new logical NLG task using open-domain tables, with input as a table T and natural language description Y.
• �� Collect and annotate a large dataset LOGICNLG, incorporating diverse logical inference types.
• �� Develop models based on LSTM, Transformer, GPT-2, and BERT, with table encoding strategies like field-infusing and field-gating.
• �� Implement training algorithms: maximum likelihood, reinforcement learning, adversarial regularization.
• �� Introduce a coarse-to-fine generation scheme: first generate a logical template YT, then realize the final sentence Y, to address order mismatch.
• �� Use semantic parsing and entailment models (Table-BERT) for logical verification, computing metrics like SP-Acc and NLI-Acc.
• �� Conduct comprehensive experiments, ablation studies, and human evaluations to validate performance.

The LOGICNLG dataset, derived from TabFact, contains over 28,000 training, validation, and test examples across multiple domains. Models trained with Adam optimizer, with hyperparameters tuned for stability and performance. Evaluation metrics include BLEU, Perplexity, and novel logical fidelity scores. Baseline comparisons involve non-pretrained models (LSTM, Transformer) and pre-trained models (GPT-2, BERT). Ablation studies analyze the impact of training strategies and architecture choices. Human evaluations assess logical correctness and fluency, confirming the automatic metrics' validity. Results demonstrate that pre-trained models with coarse-to-fine generation outperform baselines, achieving higher logical fidelity and language quality.

Pre-trained models like GPT-2 and BERT outperform non-pretrained baselines by over 15% in BLEU and logical fidelity scores. The coarse-to-fine scheme improves logical accuracy by approximately 10%, while maintaining fluency. RL and adversarial training strategies enhance logical consistency, with adversarial regularization improving robustness against logical perturbations. Ablation results confirm that combining multiple strategies yields the best overall performance. Human evaluations align with automatic metrics, validating the approach’s effectiveness across diverse logical inference types.

This framework can be directly applied to intelligent question answering, automated report generation, and knowledge graph construction, where logical correctness is crucial. It enables AI systems to generate explanations and summaries that are both fluent and logically supported, improving trustworthiness. Future deployment may involve integration with multimodal data, enabling reasoning over images, videos, and structured data, broadening AI’s reasoning capabilities in real-world scenarios.

Current models still struggle with multi-hop reasoning and handling ambiguous or conflicting facts, especially in long texts. The reliance on pre-trained models introduces biases and limits interpretability. High computational costs for training large models and complex inference pipelines hinder scalability. Future work should focus on improving reasoning depth, reducing resource demands, and developing more transparent evaluation metrics to better reflect true logical understanding.