RDF-to-Text Generation with Reinforcement Learning Based Graph-augmented Structural Neural Encoders
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by
Hanning Gao, Lingfei Wu, Hongyun Zhang, Zhihua Wei, Po Hu, Fangli Xu, Bo Long
2022
Abstract
Considering a collection of RDF triples, the RDF-to-text generation task aims
to generate a text description. Most previous methods solve this task using a
sequence-to-sequence model or using a graph-based model to encode RDF triples
and to generate a text sequence. Nevertheless, these approaches fail to clearly
model the local and global structural information between and within RDF
triples. Moreover, the previous methods also face the non-negligible problem of
low faithfulness of the generated text, which seriously affects the overall
performance of these models. To solve these problems, we propose a model
combining two new graph-augmented structural neural encoders to jointly learn
both local and global structural information in the input RDF triples. To
further improve text faithfulness, we innovatively introduce a reinforcement
learning (RL) reward based on information extraction (IE). We first extract
triples from the generated text using a pretrained IE model and regard the
correct number of the extracted triples as the additional RL reward.
Experimental results on two benchmark datasets demonstrate that our proposed
model outperforms the state-of-the-art baselines, and the additional
reinforcement learning reward does help to improve the faithfulness of the
generated text.
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