通过强化学习优化知识系统学习请求选择，提升问答性能。
A Coordination-based Approach for Focused Learning in Knowledge-Based Systems
- 将学习请求选择建模为协调博弈，用强化学习求解最优策略。
- 实验表明该方法可显著提升知识系统的问答表现。
- 适合关注知识系统自我优化与学习策略设计的研究者。
近年来，基于阅读的学习和机器阅读系统显著提升了知识系统获取新事实的能力。本文探讨如何为这些知识系统选择一组学习请求，以实现最佳问答性能。为理解该问题的动态特性，我们模拟了向外部知识源发送学习请求的学习策略。结果表明，选择最优的事实集合类似于协调博弈问题，并采用强化学习方法解决。实验显示，该方法能显著提升问答性能。
原文摘要 · Abstract (English)
Recent progress in Learning by Reading and Machine Reading systems has significantly increased the capacity of knowledge-based systems to learn new facts. In this work, we discuss the problem of selecting a set of learning requests for these knowledge-based systems which would lead to maximum Q/A performance. To understand the dynamics of this problem, we simulate the properties of a learning strategy, which sends learning requests to an external knowledge source. We show that choosing an optimal set of facts for these learning systems is similar to a coordination game, and use reinforcement learning to solve this problem. Experiments show that such an approach can significantly improve Q/A performance.
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