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.- Anthology ID:
- C18-1028
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 331–342
- Language:
- URL:
- https://aclanthology.org/C18-1028
- DOI:
- Cite (ACL):
- Seid Muhie Yimam and Chris Biemann. 2018. Par4Sim – Adaptive Paraphrasing for Text Simplification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 331–342, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Par4Sim – Adaptive Paraphrasing for Text Simplification (Yimam & Biemann, COLING 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/C18-1028.pdf