Par4Sim – Adaptive Paraphrasing for Text Simplification

Seid Muhie Yimam, Chris Biemann


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
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
331–342
Language:
URL:
https://aclanthology.org/C18-1028
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/C18-1028.pdf