# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
###### Abstract  
Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA. Chinese knowledge base; question answering system; BERT; transfer learning; CNN +
Footnote †: journal:  
gradually becoming a hot spot in the area of natural language processing. Knowledge graphs are a special type of semantic network, which represents entities in the objective world and their relationships in graphic forms. Generally, virtual knowledge exists in the form of triples, such as <entity, predicate, target value>, which can help organize, manage, and comprehend the vast amount of information available online. Most of the knowledge on the web is unstructured or semi-structured, organized in a way that is suitable for people to read and understand but not computer-friendly [5]. Knowledge graphs are helpful in constructing heterogeneous knowledge in the domain and establishing inter-knowledge associations. Google, Facebook, and Baidu have developed some knowledge graphs, and demonstrated their value in many ways. KBQA [6] utilizes knowledge graphs as one of the sources of knowledge to understand the questions using natural language input from users and identify the entities and predicates to find the corresponding target values as answers. Existing mainstream approaches to Chinese Knowledge Base Question Answering (CKBQA) usually split the question and answering task into two subtasks: entity linking and predicate matching. Regarding the entity linking module, [PERSON] et al. [7] used convolutional neural networks(CNN) and Gate Recurrent Unit(GRU) models to obtain semantic representations of questions. [PERSON] et al. [8] used CNN to develop Named Entity Recognition (NER) and Bidirectional Long Short-Term Memory (BiLSTM) and CNN to implement predicate mapping. [PERSON] et al. [9] reported their progress in NER by extracting various features, using Gradient Boosting Decision Tree (GBDT) model exploration, and using Naive Bayes Support Vector Machine (NBSVM) and CNN support to design predicate mapping. [PERSON] et al. [10] generated candidate entities through an alias dictionary, constructed artificial rules for entity disambiguation, and calculated cosine similarity based on word vectors to score predicates. With the appearance of the pre-trained language model Bidirectional Encoder Representation from Transformers(BERT), [PERSON] et al.