Recognising if a relation holds between two entities in a text plays a vital role in information extraction. To address this problem, multiple models have been proposed based on fixed or contextualised word representations. In this paper, we propose a meta relation classification model that can integrate the most recent models by the use of a related task, namely relation validation. To do so, we encode the text that may contain the relation and a relation triplet candidate into a sentence-triplet representation. We grounded our strategy in recent neural architectures that allow single sentence classification as well as pair comparisons. Finally, our model is trained to determine the most relevant sentence-triplet pair from a set of candidates. Experiments on two public data sets for relation extraction show that the use of the sentence-triplet representation outperforms strong baselines and achieves comparable results when compared to larger models.
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