# Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training
## 4 Experimental Results and Analysis
### Experimental Results and Analysis  
Currently, the most commonly used evaluation criteria for NER are Precision, Recall, and F1-score. In order to verify the effectiveness of this paper's model for the cross-lingual named entity recognition task, a comparison experiment of different named entity recognition models is conducted. The experiment was conducted on the same data set using CRF \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Language** & **Train** & **Dev** & **Test** \\ \hline CoNLL2003 & English & 204,567 & 51,578 (5942) & 46,666 (5648) \\ WeiboNER & Chinese & 1350 & 270 & 270 \\ PeopleDaily2004 & Chinese & 28,046 & 4636 & 4636 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: NER dataset. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Epoch & 3 \\ Batch size & 20 \\ Learning rate & \(4\times 10^{-5}\) \\ Dropout & 0.5 \\ Token embedding dimension & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameters. model, Pipeline model, and M-Bert+Bi-LSTM-CRF model for the cross-lingual NER task, respectively, and the results are listed in Table 3.  
Compared with the traditional CRF-based model and the traditional Pipeline-based [26] named entity recognition model, the performance of the model in this paper is also improved greatly, indicating the effectiveness of the framework in the task of cross-language named entity recognition. In order to study the influence of different word vector representation methods on the experimental results, the experiment also selects Word2 Vec [27], ELMO [28], and BERT, three mainstream word vector representation methods, for a comparative test. Word2 Vec is a static word vector representation method considering the context-free information of words. Both ELMO and BERT are dynamic word vector representation methods that can fully represent the contextual semantic and syntactic information of words. The difference is that ELMO can only take into account the unidirectional semantic information of words, while BERT can fully integrate the semantic information of words in both directions of context. The experimental results prove the advantage of using the pre-trained M-BERT [29] model for vector representation in the cross-language named entity recognition model. From Table 4, this model achieves an F1 value of 53.22% in the People-Daily2004 data set, which is 6.29% better than the baseline model, and an F1 value of 53.71% in the WeiboNER data set, which is 3.39% better than the baseline model, outperforming the other comparison models on both datasets. The experimental results show that the model in this paper has a significant improvement in performance and is able to learn the deep to be information of the text, which effectively improves the cross-language migration model. In addition, compared with [PERSON] et al. (2018) [30], who only apply a shared context encoder to transfer the knowledge, our approach not only includes a language-sharing encoder, but also performs word-level adversarial training to encourage the semantic alignment of words from both languages and a sequence encoder to extract language-agnostic sequential features [31]. To investigate the contribution of attention mechanisms to the model, ablation experiments were designed to further analyze the model in this paper. As can be seen from Table 5, the F1 values decreased by 1.8% and 4.8% when the model did not use the attention mechanism. The results show that using the attention mechanism can make full use of the information carried by the input sequence, prevent the model from losing the original1 information with training, and make the model better for cross-linguistic transfer. From the experiments where the location information was removed, it can be observed that the F1 values decreased by 0.85% and 1.08%. It can be seen that the incorporation of location information has some improvement on the model effect.