MULTI-CELL LSTM

Multi-Cell LSTM是一种用于NER领域适应的模型结构，它在传统的LSTM基础上进行了改进。该模型使用了Entity typed LSTM cells (ET cells)和Compositional LSTM cell (C cell)来处理输入数据。\[2\]与传统的LSTM不同，Multi-Cell LSTM在每个维度上都有多个cell，每个cell都会输出多个隐藏状态和记忆状态。这些隐藏状态和记忆状态在不同维度的cell之间通过LSTM cell的公式相连，而不是像Multi-Dimensional LSTM那样只有一个输出。\[3\]这种设计可以增加模型的稳定性和表达能力，提高NER任务的性能。
#### 引用[.reference_title]
- *1* *3* [RNN, LSTM, GRU, SRU, Multi-Dimensional LSTM, Grid LSTM, Graph LSTM系列解读](https://blog.csdn.net/u013010889/article/details/79507177)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [【论文阅读ACL2020】Multi-Cell Compositional LSTM for NER Domain Adaptation](https://blog.csdn.net/weixin_46276006/article/details/115101609)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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