基于伪标签和迁移学习的双关语识别方法

Pun detection basd on pseudo-label and transfer learning

针对双关语样本短缺问题,研究提出了基于伪标签和迁移学习的双关语识别模型(pun detection based on Pseudo-label and transfer learning)。该模型利用上下文语义、音素向量和注意力机制生成伪标签;然后,迁移学习和置信度结合挑选可用的伪标签;最后,将伪标签数据和真实数据混合到网络中进行训练,重复伪标签标记和混合训练过程。一定程度上解决了双关语样本量少且获取困难的问题。使用该模型在SemEval 2017 shared task 7以及Pun of the Day数据集上进行双关语检测实验,结果表明模型性能均优于现有主流双关语识别方法。

To address the problem of shortage of the pun samples,this paper proposes a pun recognition model based on pseudo-label speech-focused context(pun detection based on pseudo-label and transfer learning).Firstly,the model uses contextual semantics,phoneme vector and attention mechanism to generate pseudo-labels.Then,it combines transfer learning and confidence to select useful pseudo-labels.Finally,the pseudo-label data and real data are used for network theory and training,and the pseudo-label labeling and mixed training procedures are repeated.To a certain extent,the problem of small sample size and difficulty in obtaining puns has been solved.By this model,we carry out pun detection experiments on both the SemEval 2017 shared task 7 dataset and the Pun of the Day dataset.The results show that the performance of this model is better than that of the existing mainstream pun recognition methods.