Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage.
@article{arxiv.1806.08309,
title = {Par4Sim -- Adaptive Paraphrasing for Text Simplification},
author = {Seid Muhie Yimam and Chris Biemann},
journal= {arXiv preprint arXiv:1806.08309},
year = {2018}
}