Communication, Computer and Automation Engineering
Pun detection basd on pseudo-label and transfer learning
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Abstract:
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.
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Supported by Guangzhou Science and Technology Plan Project (202102020637，202002030227) and Teacher-Student Joint Research Project on Guangdong University of Foreign Studies (21SS10).
