## 1 Introduction  
Machine reading comprehension (MRC) is a technology by which a machine can find answers to questions about a given document. RACE [1] and SQuAD [2] are passage-based reading comprehension datasets. Using such datasets, a machine can learn to find answers from a given passage. In contrast to these datasets, DREAM [3] is a dialogue-based question-answering (QA) dataset that focuses on in-depth multi-turn multi-party dialogue understanding. It consists of 6444 dialogues and 10,197 questions. Each data item consists of one dialogue, one question, three candidates, and one answer. DREAM presents more challenges than previous dialogue-based multiple-choice QA datasets. Specifically, 84% of the answers are non-extractive, 85% of the questions require multi-sentence reasoning, and 34% of the questions require commonsense reasoning. A sample from DREAM is presented in Table 1. Thus, a high level of commonsense reasoning and a deep understanding of dialogue are required to improve performance. Therefore, we propose a Dialogue-based QA model with Common-sense Reasoning (DQACR).  
Fine-tuning of pre-trained language models (PTLMs) using dialogue-based QA datasets has been shown to be effective [4, 5]. However, this method has several drawbacks. If the input sequence is longer than the number of tokens that the model can take, the rear part of the dialogue history becomes truncated. A PTLM contains a fixed number of tokens that can be received. For example, BERT [6] can receive up to 512 tokens. Such truncation can result in information loss, with subsequent degradation of performance owing to the inadequate input of important information. Furthermore, because PTLMs use only the given dialogue history to find the answers to questions, it is difficult to implement them for solving problems that require commonsense reasoning.  
Using Semantic Search (SS), DQACR identifies sentences relevant to the questions within the dialogue history. We can reduce the information loss caused by truncation if the selected sentences are used as the input instead of the entire dialogue history. However, problems remain with the commonsense reasoning of the model.  
Commonsense is an inherent trait of humans. However, machines cannot acquire commonsense without learning-related knowledge [7]. Continual learning and Semantic Search can improve the commonsense reasoning of a machine. Continual learning is a method in which a PTLM is fine-tuned with a task required to perform the current task in advance. By pre-training a model with CommonsenseQA [8] (CSQA), a typical commonsense inference task, we can improve the commonsense reasoning of the model. ConceptNet [9] can also be used to improve commonsense reasoning. From Refs. [10,11], we know that such an approach can help improve commonsense reasoning performance. Considering ConceptNet knowledge about the problem, the model can read a dialogue with commonsense. This process can be useful in terms of gaining a deep understanding of dialogue and improving commonsense reasoning.  
The main contributions of this study are as follows:  
* Commonsense reasoning of a PTLM can be improved by learning commonsense through continual learning.
* Semantic Search is used to reduce the information loss in the dialogue history and improve commonsense reasoning using ConceptNet.
* DQACR achieves better performance than the baseline method.