Demonstrating PAR4SEM - A Semantic Writing Aid with Adaptive Paraphrasing
This work addresses the challenge of creating ever-learning NLP tools for practical writing assistance, though it appears incremental as it builds on existing adaptive methods.
The authors tackled the problem of integrating machine learning into real-world writing aids by developing Par4Sem, a tool that uses adaptive paraphrasing to collect training data from user interactions, and demonstrated its application in text simplification through usage-based evaluation.
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.