CEFR-oriented Russian Text Simplification Using Reinforcement Learning

Key points

• We fine-tune YandexGPT using Rejection Sampling and Direct Preference Optimization (DPO) methods to improve control over the complexity of the generated text.1
• We compare several training configurations that differ in dataset composition, text type and the strategy used to construct preference pairs for DPO.1
• For automatic evaluation we train a regression model based on ruRoBERTa-large to predict continuous complexity scores corresponding to CEFR levels.1
• We also consider semantic similarity, lexical coverage and quantitative readability metrics.1

Sources (1)

• [1]CEFR-oriented Russian Text Simplification Using Reinforcement Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:04 PM

  We present a study on controlled automatic simplification of Russian texts using preference-based optimization algorithms with a focus on adapting texts to the A1, A2 and B1 proficiency levels.

  We fine-tune YandexGPT using Rejection Sampling and Direct Preference Optimization (DPO) methods to improve control over the complexity of the generated text.

Extractive summary: sentences quoted from the sources.