Demonstrating PAR4SEM - A Semantic Writing Aid with Adaptive Paraphrasing

TL;DR

This paper introduces Par4Sem, an adaptive semantic writing aid that iteratively improves its paraphrasing models through real-world usage data, demonstrating its effectiveness in text simplification tasks.

Contribution

It presents Par4Sem, a novel adaptive paraphrasing tool integrated into a writing aid, capable of updating its models based on user interactions in real time.

Findings

Effective in collecting training data from usage

Improves paraphrasing models iteratively

Enhances text simplification performance

Abstract

In this paper, we present Par4Sem, a semantic writing aid tool based on adaptive paraphrasing. Unlike many annotation tools that are primarily used to collect training examples, Par4Sem is integrated into a real word application, in this case a writing aid tool, in order to collect training examples from usage data. Par4Sem is a tool, which supports an adaptive, iterative, and interactive process where the underlying machine learning models are updated for each iteration using new training examples from usage data. After motivating the use of ever-learning tools in NLP applications, we evaluate Par4Sem by adopting it to a text simplification task through mere usage.