基于深度强化学习的维吾尔语人称代词指代消解

针对深度神经网络模型仅学习当前指代链语义信息忽略了单个指代链识别结果的长期影响问题，提出一种结合深度强化学习（deep reinforcement learning）的维吾尔语人称代词指代消解方法.该方法将指代消解任务定义为强化学习环境下顺序决策过程，有效利用之前状态中先行语信息判定当前指代链指代关系.同时，采用基于整体奖励信号优化策略，相比于使用损失函数启发式优化特定的单个决策，该方法直接优化整体评估指标更加高效.最后在维吾尔语数据集进行实验，实验结果显示，该方法在维吾尔语人称代词指代消解任务中的F值为85.80%.实验结果表明，深度强化学习模型能显著提升维吾尔语人称代词指代消解性能.

Anaphora Resolution of Uyghur Personal Pronouns Based on Deep Reinforcement Learning

Deep neural network models for Uyghur personal pronouns resolution learn semantic information for current anaphora chain,but ignore the long-term effects of single anaphora chain recognition results.This paper proposes a Uyghur personal pronoun anaphora resolution based on deep reinforcement learning.This method defines the anaphora resolution task as the sequential decision process under the reinforcement learning environment,and effectively uses the antecedent information in the previous state to determine the current personal pronoun-candidate antecedent pairs.In this study,we use an overall reward signal optimization strategy,which is more efficient than directly using the loss function heuristic to optimize a specific single decision.Finally,we conduct experiments in the Uyghur dataset.The experimental results show that the F value of this method in the Uyghur personal pronouns resolution task is 85.80%.The experimental results show that the deep reinforcement learning model can significantly improve the performance of the Uyghur personal pronouns resolution.