Abstract
We present an architecture that generates medical texts while learning an informative, continuous representation with discriminative features. During training the input to the system is a dataset of captions for medical X-Rays. The acquired continuous representations are of particular interest for use in many machine learning techniques where the discrete and high-dimensional nature of textual input is an obstacle. We use an Adversarially Regularized Autoencoder to create realistic text in both an unconditional and conditional setting. We show that this technique is applicable to medical texts which often contain syntactic and domain-specific shorthands. A quantitative evaluation shows that we achieve a lower model perplexity than a traditional LSTM generator.- Anthology ID:
- N18-5014
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 66–70
- Language:
- URL:
- https://aclanthology.org/N18-5014
- DOI:
- 10.18653/v1/N18-5014
- Cite (ACL):
- Graham Spinks and Marie-Francine Moens. 2018. Generating Continuous Representations of Medical Texts. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 66–70, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Generating Continuous Representations of Medical Texts (Spinks & Moens, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/N18-5014.pdf