@InProceedings{yimam-biemann:2018:C18-1,
  author    = {Yimam, Seid Muhie  and  Biemann, Chris},
  title     = {Par4Sim -- Adaptive Paraphrasing for Text Simplification},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {331--342},
  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.},
  url       = {http://www.aclweb.org/anthology/C18-1028}
}