Par4Sim -- Adaptive Paraphrasing for Text Simplification

Seid Muhie Yimam; Chris Biemann

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arXiv · 1806.08309

Par4Sim -- Adaptive Paraphrasing for Text Simplification

Seid Muhie Yimam·Chris Biemann

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Abstract

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.

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Seid Muhie Yimam, Chris Biemann. 2018-06-21. Par4Sim -- Adaptive Paraphrasing for Text Simplification. https://arxiv.org/abs/1806.08309

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