# Transforming Complex Sentences into a Semantic Hierarchy

## TL;DR

This paper introduces a recursive method for transforming complex sentences into a semantic hierarchy of simplified sentences, improving interpretability and performance in AI tasks like machine translation and information extraction.

## Contribution

It presents a novel hierarchical sentence simplification approach using handcrafted rules that enhances structural simplicity and preserves semantic relationships, outperforming existing methods.

## Key findings

- Outperforms state-of-the-art in structural text simplification
- Improves Open IE system performance by up to 346% in precision
- Enhances interpretability for downstream AI applications

## Abstract

We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.