基于伪标签和迁移学习的双关语识别方法

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。

Pun detection basd on pseudo-label and transfer learning

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.