Data-to-text generation is a rapidly evolving field that seeks to transform structured data into coherent and natural language descriptions. Despite recent advances, systems still struggle when confronted with unseen combinations of predicates, resulting in unfaithful descriptions (i.e., hallucinations or omissions). To address this issue, we propose a novel approach that leverages predicate decomposition to improve compositional generalization. Our approach clusters predicates into groups and generates text sentence by sentence, relying on one cluster at a time.
Predicate Decomposition
At the heart of our approach is the concept of predicate decomposition. By decomposing predicates into simpler concepts, we can better understand their relationships and generate more faithful descriptions. We use a novel clustering algorithm that groups similar predicates together, resulting in M clusters. Each cluster represents a distinct concept or category of predicates.
Few-shot Learning
To evaluate the effectiveness of our approach, we conduct a series of experiments using various few-shot learning scenarios. In each scenario, we train a model on a small set of seen examples and test its performance on a larger set of unseen examples. We use a combination of evaluation metrics, including grammaticality, repetition, hallucination, and omission, to assess the models’ performance.
Results
Our experiments show that our novel approach outperforms the T5 baseline across all evaluation metrics. In particular, we achieve a 31% improvement in terms of a metric focused on maintaining faithfulness to the input. This suggests that our approach is effective at generating more accurate and informative descriptions.
Conclusion
In this article, we have demystified data-to-text generation by proposing a novel approach to compositional generalization. By leveraging predicate decomposition and few-shot learning, we can generate more faithful and informative descriptions of structured data. Our experiments show that our approach outperforms existing models, demonstrating its effectiveness in improving data-to-text generation. As the field continues to evolve, we believe that this approach will play an increasingly important role in enabling machines to communicate more effectively with humans.
