# AidIR: An Interactive Dialog System to Aid Disease Information Retrieval
## 6 Conclusions  
AidIR enables users to retrieve information on diseases related to coronaviruses and diseases transmitted by vector mosquitoes by using natural language interaction and Line chat medium. In subjective evaluation, we asked users to rate AidIR in the range between \(-2\) and \(2\). The average scores of intuitiveness, usability, and user experiences were 0.8, 0.8, and 1.05, respectively. Moreover, we asked those users to evaluate AidIR by answering yes-no questions. The results show that AidIR is better than systems using GUI and single-turn dialog systems. Furthermore, most of the users will use systems such as AidIR to retrieve information. Finally, we asked those users to provide the upsides and downsides of AidIR. Most users thought that AidIR was more convenient for information retrieval. In addition to subjective evaluation, we also compared AidIR with systems using GUI. Systems using GUI cannot guide users to provide specific information. This system will only return as much information as possible. Since systems with GUI do not have a tracking system, users need to provide the same information and they cannot rectify the error of the system. Furthermore, systems with GUI do not exploit the context to retrieve the information and tokenize the words. This mechanism results in general information. As a result, end-users may feel frustrated since end-users usually prefer specific information.  
In the pre-processing stage, we operationalized the taxonomy and replace some types of numbers with special tokens. In the taxonomy operationalization stage, we found that some diseases have similar features. These characteristics may hinder the classification of the diseases. Therefore, we operationalized the diseases with similar characteristics into the same category. In the token replacement stage, we wrote regular expressions to match some categories of numbers and replaced them with special tokens to normalize the sentence. According to our experiments, this method yields better results in both the mask LM task and the downstream task. Specifically, the output probability in the mask LM task is more peak when we replace some types of numbers with special tokens. In terms of the downstream task, the accuracy of the ABERT model trained with these special tokens is 12% higher in the domain classification task and 5% higher in the sequence label task. The reason for which the accuracy of the ABERT model in the sequence label task only slightly decays is because the weight of the sequence label task is greater than that of the domain classification task. Therefore, these experimental results demonstrate the effectiveness of our methods.  
In terms of NLU models, we trained a BERT model to perform domain classification and sequence labels simultaneously. To this end, we use a loss function with the weighting method that we design to train the model for two tasks. The accuracy of both tasks reaches 92%. Furthermore, our weighting method enables us to give the task that we want to enhance its accuracy with a higher weight. According to our experiments, we can enlarge the accuracy of the task by increasing the weight of that task. However, we need to find the trade-off. That is, we cannot overvalue one task and undervalue another task. Moreover, we examine whether fine-tuning the model with disease corpus and the mask LM task produces a better result. The experiment demonstrates that the accuracy of the model, fine-tuned with disease corpora and the mask LM task, is 24% higher in the domain classification task and 3% higher in the sequence label task.  
To make the system further learn the dialog policy, we deployed the RL algorithm to AidIR. To prevent bad interaction in the beginning, we warmed up the model with the SL task. Then, we employed the A2C algorithm to enable AidIR to continue learning the dialog policy online. Moreover, we parallelized this process to prevent this process from influencing the interaction between users and AidIR.