Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm
This addresses relation extraction in NLP, identifying noise and re-representation as key issues, but it is incremental as it builds on existing capsule network methods.
The paper tackles sentential relation extraction by proposing a dynamic routing in capsules approach, which outperforms state-of-the-art methods on datasets like Tacred and Conll04 but performs poorly on the larger Wikidata dataset due to label noise and re-representation challenges.
Sentential relation extraction (RE) is an important task in natural language processing (NLP). In this paper we propose to do sentential RE with dynamic routing in capsules. We first show that the proposed approach outperform state of the art on common sentential relation extraction datasets Tacred, Tacredrev, Retacred, and Conll04. We then investigate potential reasons for its good performance on the mentioned datasets, and yet low performance on another similar, yet larger sentential RE dataset, Wikidata. As such, we identify noise in Wikidata labels as one of the reasons that can hinder performance. Additionally, we show associativity of better performance with better re-representation, a term from neuroscience referred to change of representation in human brain to improve the match at comparison time. As example, in the given analogous terms King:Queen::Man:Woman, at comparison time, and as a result of re-representation, the similarity between related head terms (King,Man), and tail terms (Queen,Woman) increases. As such, our observation show that our proposed model can do re-representation better than the vanilla model compared with. To that end, beside noise in the labels of the distantly supervised RE datasets, we propose re-representation as a challenge in sentential RE.