Improving Compositional Generalization with Self-Training for Data-to-Text Generation

TL;DR

This paper addresses the challenge of compositional generalization in data-to-text generation by proposing a template-based input method and a self-training approach, significantly improving model performance in few-shot scenarios.

Contribution

It introduces a template-based input representation and a self-training method with BLEURT for pseudo response selection to enhance generalization in data-to-text tasks.

Findings

46%+ improvement in tree accuracy on SGD and Weather benchmarks

73%+ reduction in slot error rates in few-shot settings

Significant performance gains over T5 baselines

Abstract

Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). Such representations are compositional and it is costly to collect responses for all possible combinations of atomic meaning schemata, thereby necessitating few-shot generalization to novel MRs. In this work, we systematically study the compositional generalization of the state-of-the-art T5 models in few-shot data-to-text tasks. We show that T5 models fail to generalize to unseen MRs, and we propose a template-based input representation that considerably improves the model's generalization capability. To further improve the model's performance, we propose an approach based on self-training using fine-tuned BLEURT for pseudo response selection. On the commonly-used SGD and Weather benchmarks, the proposed self-training approach improves tree accuracy by 46%+ and reduces the slot error rates by 73%+ over the strong T5 baselines in few-shot settings.