Abstract: Textual conversational agent or chatbots' development gather tremendous traction from both academia and industries in recent years. Nowadays, chatbots are widely used as an agent to communicate with a human in some services such as booking assistant, customer service, and also a personal partner. The biggest challenge in building chatbot is to build a humanizing machine to improve user engagement. Some studies show that emotion is an important aspect to humanize machine, including chatbot. In this paper, we will provide a systematic review of approaches in building an emotionally-aware chatbot (EAC). As far as our knowledge, there is still no work focusing on this area. We propose three research question regarding EAC studies. We start with the history and evolution of EAC, then several approaches to build EAC by previous studies, and some available resources in building EAC. Based on our investigation, we found that in the early development, EAC exploits a simple rule-based approach while now most of EAC use neural-based approach. We also notice that most of EAC contain emotion classifier in their architecture, which utilize several available affective resources. We also predict that the development of EAC will continue to gain more and more attention from scholars, noted by some recent studies propose new datasets for building EAC in various languages.

Introduction: Conversational agents or dialogue systems development are gaining more attention from both industry and academia BIBREF0 , BIBREF1 in the latest years. Some works tried to model them into domain-specific tasks such as customer service BIBREF2 , BIBREF3 , and shopping assistance BIBREF4 . Other works design a multi-purpose agents such as SIRI, Amazon Alexa, and Google Assistance. This domain is a well-researched area in Human-Computer Interaction research community but still become a hot topic now. The main development focus right now is to have an intelligent and humanizing machine to have a better engagement when communicating with human BIBREF5 . Having a better engagement will lead to higher user satisfaction, which becomes the main objective from the industry perspective. In this study, we will only focus on textual conversational agent or chatbot, a conversational artificial intelligence which can conduct a textual communication with a human by exploiting several natural language processing techniques. There are several approaches used to build a chatbot, start by using a simple rule-based approach BIBREF6 , BIBREF7 until more sophisticated one by using neural-based technique BIBREF8 , BIBREF9 . Nowadays, chatbots are mostly used as customer service such as booking systems BIBREF10 , BIBREF11 , shopping assistance BIBREF3 or just as conversational partner such as Endurance and Insomnobot . Therefore, there is a significant urgency to humanize chatbot for having a better user-engagement. Some works were already proposed several approaches to improve chatbot's user-engagement, such as building a context-aware chatbot BIBREF12 and injecting personality into the machine BIBREF13 . Other works also try to incorporate affective computing to build emotionally-aware chatbots BIBREF2 , BIBREF14 , BIBREF15 . Some existing studies shows that adding emotion information into dialogue systems is able to improve user-satisfaction BIBREF16 , BIBREF17 . Emotion information contribute to a more positive interaction between machine and human, which lead to reduce miscommunication BIBREF18 . Some previous studies also found that using affect information can help chatbot to understand users' emotional state, in order to generate better response BIBREF19 . Not only emotion, another study also introduce the use of tones to improve satisfactory service. For instance, using empathetic tone is able to reduces user stress and results in more engagement. BIBREF2 found that tones is an important aspect in building customer care chatbot. They discover eight different tones including anxious, frustrated, impolite, passionate, polite, sad, satisfied, and empathetic. In this paper, we will try to summarize some previous studies which focus on injecting emotion information into chatbots, on discovering recent issues and barriers in building engaging emotionally-aware chatbots. Therefore, we propose some research questions to have a better problem definition: This paper will be organized as follows: Section 2 introduces the history of the relation between affective information with chatbots. Section 3 outline some works which try to inject affective information into chatbots. Section 4 summarizes some affective resources which can be utilized to provide affective information. Then, Section 5 describes some evaluation metric that already applied in some previous works related to emotionally-aware chatbots. Last Section 6 will conclude the rest of the paper and provide a prediction of future development in this research direction based on our analysis.

Question: What is triangulation?
Answer: Answer with content missing: (Chapter 3) The concept can be easily explained with an example, visualized in Figure 1. Consider the Portuguese (Pt) word trabalho which, according to the MUSE Pt–En dictionary, has the words job and work as possible En translations. In turn, these two En words can be translated to 4 and 5 Czech (Cs) words respectively. By utilizing the transitive property (which translation should exhibit) we can identify the set of 7 possible Cs translations for the Pt word trabalho.