Policy Learning with a Natural Language Action Space: A Causal Approach
This work addresses policy learning for complex language tasks with limited data, offering a practical solution for applications such as mental health and hate speech moderation, though it is incremental as it builds on existing Q-learning and embedding methods.
The paper tackles multi-stage decision-making in natural language action spaces with delayed rewards by introducing a causal framework using Q-learning and Dynamic Treatment Regimes, achieving significant improvements in tasks like mental health intervention and hate speech countering with superior transfer strength and content preservation.
This paper introduces a novel causal framework for multi-stage decision-making in natural language action spaces where outcomes are only observed after a sequence of actions. While recent approaches like Proximal Policy Optimization (PPO) can handle such delayed-reward settings in high-dimensional action spaces, they typically require multiple models (policy, value, and reward) and substantial training data. Our approach employs Q-learning to estimate Dynamic Treatment Regimes (DTR) through a single model, enabling data-efficient policy learning via gradient ascent on language embeddings. A key technical contribution of our approach is a decoding strategy that translates optimized embeddings back into coherent natural language. We evaluate our approach on mental health intervention, hate speech countering, and sentiment transfer tasks, demonstrating significant improvements over competitive baselines across multiple metrics. Notably, our method achieves superior transfer strength while maintaining content preservation and fluency, as validated through human evaluation. Our work provides a practical foundation for learning optimal policies in complex language tasks where training data is limited.