# Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm

## TL;DR

This paper introduces a capsule-based dynamic routing approach for sentential relation extraction, demonstrating improved performance on several datasets and highlighting the importance of re-representation inspired by neuroscience.

## Contribution

The paper proposes a novel re-representation method using dynamic routing in capsules for sentential RE, outperforming existing models on key datasets.

## Key findings

- Outperforms state-of-the-art on Tacred, Tacredrev, Retacred, and Conll04 datasets.
- Low performance on Wikidata due to label noise.
- Re-representation improves similarity matching in relation extraction.

## Abstract

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.