Text style transfer between classical and modern chinese through prompt-based reinforcement learning

Xu, Minzhang; Peng, Min; Liu, Fang

doi:10.1007/s11280-022-01083-6

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…deleting peripheral information irrelevant to the target domain) (Alva-Manchego et al 2020). Our approach is similar to the textto-text models that exert controllability using task-specific prompts (Keskar et al 2019;Dathathri et al 2019;Kariuk and Karamshuk 2020;Brown et al 2020;Reif et al 2021;Lyu et al 2021;Xu, Peng, and Liu 2022).…”

Section: Related Workmentioning

confidence: 99%

Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts

Basu

Rosni

Yasunaga

et al. 2023

AAAI

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Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present Med-EASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its expert-layman-AI collaborative annotations facilitate controllability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of input-output combinations, leading to two control-free and two controllable versions of the model. We add two types of controllability into text simplification, by using a multi-angle training approach: position-aware, which uses in-place annotated inputs and outputs, and position-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, our position-aware control enhances the model's ability to generate better simplification than the position-agnostic version. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.

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