Logical Natural Language Generation from Open-Domain Tables (2004.10404v2)

Abstract: Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language. In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t.\ logical inference. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order. In our experiments, we comprehensively survey different generation architectures (LSTM, Transformer, Pre-Trained LM) trained with different algorithms (RL, Adversarial Training, Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained LM can significantly boost both the fluency and logical fidelity metrics, 2) RL and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine generation can help partially alleviate the fidelity issue while maintaining high language fluency. The code and data are available at \url{https://github.com/wenhuchen/LogicNLG}.

Logical Natural Language Generation from Open-Domain Tables

The paper "Logical Natural Language Generation from Open-Domain Tables" introduces a crucial advancement in natural language generation (NLG) by proposing a task aimed at generating logically entailed natural language statements from open-domain semi-structured tables. Traditional NLG models have been predominantly focused on achieving fluency and coherence in text generation, often neglecting the aspect of logical inference, which is an integral part of human reasoning. This paper highlights the need to incorporate logical inference into NLG tasks, hence proposing a more complex scenario where models need to extend beyond surface-level fact restatements.

Dataset and Approach

The authors utilize the TabFact dataset, which is robust with logical and symbolic inferences, to test their proposed logical NLG task. They propose innovative automatic metrics to evaluate the fidelity of generation models concerning logical inference. The work highlights the challenges posed by logical NLG, particularly due to the discrepancies between sequence order and logical order, which traditional monotonic generation frameworks fail to address adequately.

In their empirical investigation, the authors explore various generation architectures including LSTM, Transformer, and Pre-Trained LLMs (Pre-Trained LMs), and train them using different algorithms, such as Reinforcement Learning (RL), Adversarial Training, and Coarse-to-Fine generation. The key observations from their experiments underscore the efficacy of Pre-Trained LMs in enhancing both fluency and logical fidelity. Notably, RL and Adversarial Training show a tendency to trade off fluency for fidelity. The Coarse-to-Fine generation approach partly mitigates fidelity issues while sustaining language fluency.

Key Findings and Evaluations

1. Impact of Pre-Trained Models: Pre-Trained LMs such as GPT-2 demonstrate significant improvements in logical fidelity metrics. This suggests a promising avenue in leveraging the vast knowledge embedded in these large models to improve NLG tasks that require more than superficial fact restatement.
2. Training Methodologies: The exploration of RL and Adversarial Training highlights a trade-off scenario wherein RL and Adversarial Training frameworks improve fidelity but at the cost of fluency. This distinct trade-off is a critical finding, as it delineates the boundary of what current methodologies can achieve in logical NLG tasks.
3. Evaluation Mechanisms: The introduction of Parsing-Based Evaluation and NLI-Based Evaluation for logical consistency and fidelity highlights the paper's innovative approach to evaluating logical NLG, which traditionally relies heavily on fluency metrics such as BLEU. These new metrics are crucial given the task's emphasis on logic.

Implications and Future Direction

The paper's contributions lie not only in highlighting the limitations of current NLG models regarding logical inference but also in setting the stage for future explorations into more sophisticated and non-monotonic generation frameworks. The proposed task and evaluation metrics pave the way for delineating logical NLG's unique challenges, which require reasoning capabilities akin to semantic parsing.

Practically, this advancement holds significant promise for numerous applications such as data-driven storytelling, automated report generation, and enhanced interactions with knowledge bases where logical inference is vital. The proposed approach can catalyze the development of models that are not only coherent and fluent but also logically sound, ultimately leading to AI systems that better mimic human reasoning.

In conclusion, the paper sets a compelling stage for future research in logical natural language generation, offering a novel perspective that intertwines fluency with logical consistency. Future investigations could delve into approaches that seamlessly integrate logical reasoning with natural language generation, possibly harnessing enhancements in neural symbolic models or evolving transformer architectures for better contextual understanding and logical deduction.