Set-Aligning Framework for Auto-Regressive Event Temporal Graph
  Generation
Xingwei Tan Yuxiang Zhou Gabriele Pergola Yulan He
Event temporal graphs have been shown as convenient and effective
representations of complex temporal relations between events in text. Recent
studies, which employ pre-trained language models to auto-regressively generate
linearised graphs for constructing event temporal graphs, have shown promising
results. However, these methods have often led to suboptimal graph generation
as the linearised graphs exhibit set characteristics which are instead treated
sequentially by language models. This discrepancy stems from the conventional
text generation objectives, leading to erroneous penalisation of correct
predictions caused by the misalignment of elements in target sequences. To
address these challenges, we reframe the task as a conditional set generation
problem, proposing a Set-aligning Framework tailored for the effective
utilisation of Large Language Models (LLMs). The framework incorporates data
augmentations and set-property regularisations designed to alleviate text
generation loss penalties associated with the linearised graph edge sequences,
thus encouraging the generation of more relation edges. Experimental results
show that our framework surpasses existing baselines for event temporal graph
generation. Furthermore, under zero-shot settings, the structural knowledge
introduced through our framework notably improves model generalisation,
particularly when the training examples available are limited.