# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 6 Discussion  
Table 7 presented performance comparison between our method and other published methods. The systems of [PERSON] et al. [10], [PERSON] et al. [52], and [PERSON] et al. [9] are the top three for the NLPCC 2016 KBQA evaluation task, and they all combine neural networks and manually constructed rules to ensure the quality of question and answering. BB-KBQA [11] fine-tunes the pre-training task based on BERT to achieve three subtasks of NER, entity disambiguation, and predicate mapping, but the performance on the first two subtasks is weaker than the model of this paper, resulting in a lower final performance than the KBQA in this paper. The results of experiments demonstrates that the BAT-KBQA can achieve an Averaged F1 value of 87.74%, which achieves the best results and improves the question answering system accuracy compared with other published methods.  
Finally, we analyzed the effect of the CWS task of this work, and the results show that word boundary information from the CWS task is effective for the Chinese NER task. Particularly when various entities appear at the same time, our model can correctly classify the word in different scenarios. Then, the performance of the question-answering system is evaluated. The results are found accurate except for some triples and wrong answers. For example, for the question \"Where did [PERSON] debut?\" (in Chinese), corresponding to the knowledge triple <[PERSON], debut place, Taiwan>, our system predicts the subject entity and predicate as <[PERSON] (male singer from Hong Kong and Taiwan), debut place>, indicating that the entity disambiguation module can correctly select the answer [PERSON], who is a singer. For the question \"Which factory built submarine type 212?\" (in Chinese), the question \"Which factory built submarine type 212?  
Chinese), the corresponding knowledge triple is <212 type submarine, built, Hathaway Shipyard (hdw)>, and the subject entity and predicate predicted by our system is <212 type submarine, manufacturing plant>, which shows that although the predicate chosen by the predicate matching module is different from the labeled predicate, the correct predicate is chosen by understanding the intention of the question, and the correct answer is finally found. In practice, the method employs some artificial rules because of the introduction of CWS task, which requires data annotation such as the NER task. Therefore, additional effort should be spent on annotation and the need for manual labeling data is sometimes tedious. However, labeling is relatively easy and within an acceptable range.  
In summary, we can see that introduction of CWS in the NER task can enhance the accuracy of entity linking. In our work, entity disambiguation and predicate mapping combined with BERT pre-training exhibit better performance, and the entities also have a strong correlation with the predicates. Compared to other models that use more complex features and artificial rules, our KBQA system achieve better results with only neural network models and a small number of simple text features. Therefore, our system achieves sufficient accuracy and can effectively answer the questions asked by the users.