Compositional Generalization for Data-to-Text Generation
Submission Type: Regular Long Paper
Submission Track: Natural Language Generation
Keywords: Compositional Generalization, Data-to-Text Generation, Natural Language Generation, Clustering, Reinforcement Learning, Benchmark
TL;DR: A clustering-based approach aiming at improving the compositional generalisation ability of the models on Data-to-text generation tasks.
Abstract: Data-to-text generation involves transforming structured data, often represented as predicate-argument tuples, into coherent textual descriptions. Despite recent advances, systems still struggle when confronted with unseen combinations of predicates, producing unfaithful descriptions (e.g.,hallucinations or omissions). We refer to this issue as compositional generalisation, and it encouraged us to create a benchmark for assessing the performance of different approaches on this specific problem. Furthermore, we propose a novel model that addresses compositional generalization by clustering predicates into groups. Our model generates text in a sentence-by-sentence manner, relying on one cluster of predicates at a time. This approach significantly outperforms T5-baselines across all evaluation metrics. Notably, it achieved a 31% improvement over T5 in terms of a metric focused on maintaining faithfulness to the input.
Submission Number: 2193
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