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.
History of Emotionally-Aware Chatbot
The early development of chatbot was inspired by Turing test in 1950 BIBREF20 . Eliza was the first publicly known chatbot, built by using simple hand-crafted script BIBREF21 . Parry BIBREF22 was another chatbot which successfully passed the Turing test. Similar to Eliza, Parry still uses a rule-based approach but with a better understanding, including the mental model that can stimulate emotion. Therefore, Parry is the first chatbot which involving emotion in its development. Also, worth to be mentioned is ALICE (Artificial Linguistic Internet Computer Entity), a customizable chatbot by using Artificial Intelligence Markup Language (AIML). Therefore, ALICE still also use a rule-based approach by executing a pattern-matcher recursively to obtain the response. Then in May 2014, Microsoft introduced XiaoIce BIBREF23 , an empathetic social chatbot which is able to recognize users' emotional needs. XiaoIce can provide an engaging interpersonal communication by giving encouragement or other affective messages, so that can hold human attention during communication.
Nowadays, most of chatbots technologies were built by using neural-based approach. Emotional Chatting Machine (ECM) BIBREF15 was the first works which exploiting deep learning approach in building a large-scale emotionally-aware conversational bot. Then several studies were proposed to deal with this research area by introducing emotion embedding representation BIBREF24 , BIBREF25 , BIBREF26 or modeling as reinforcement learning problem BIBREF27 , BIBREF28 . Most of these studies used encoder-decoder architecture, specifically sequence to sequence (seq2seq) learning. Some works also tried to introduce a new dataset in order to have a better gold standard and improve system performance. BIBREF14 introduce EMPATHETICDIALOGUES dataset, a novel dataset containing 25k conversations include emotional contexts information to facilitate training and evaluating the textual conversational system. Then, work from BIBREF2 produce a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries. This dataset was used to build tone-aware customer care chatbot. Finally, BIBREF29 tried to enhance SEMAINE corpus BIBREF30 by using crowdsourcing scenario to obtain a human judgement for deciding which response that elicits positive emotion. Their dataset was used to develop a chatbot which captures human emotional states and elicits positive emotion during the conversation.
Building Emotionally-Aware Chatbot (EAC)
As we mentioned before that emotion is an essential aspect of building humanize chatbot. The rise of the emotionally-aware chatbot is started by Parry BIBREF22 in early 1975. Now, most of EAC development exploits neural-based model. In this section, we will try to review previous works which focus on EAC development. Table TABREF10 summarizes this information includes the objective and exploited approach of each work. In early development, EAC is designed by using a rule-based approach. However, in recent years mostly EAC exploit neural-based approach. Studies in EAC development become a hot topic start from 2017, noted by the first shared task in Emotion Generation Challenge on NLPCC 2017 BIBREF31 . Based on Table TABREF10 this research line continues to gain massive attention from scholars in the latest years.
Based on Table TABREF10 , we can see that most of all recent EAC was built by using encoder-decoder architecture with sequence-to-sequence learning. These seq2seq learning models maximize the likelihood of response and are prepared to incorporate rich data to generate an appropriate answer. Basic seq2seq architecture structured of two recurrent neural networks (RNNs), one as an encoder processing the input and one as a decoder generating the response. long short term memory (LSTM) or gated recurrent unit (GRU) was the most dominant variant of RNNs which used to learn the conversational dataset in these models. Some studies also tried to model this task as a reinforcement learning task, in order to get more generic responses and let the chatbot able to achieve successful long-term conversation. Attention mechanism was also introduced in this report. This mechanism will allow the decoder to focus only on some important parts in the input at every decoding step.
Another vital part of building EAC is emotion classifier to detect emotion contained in the text to produce a more meaningful response. Emotion detection is a well-established task in natural language processing research area. This task was promoted in two latest series of SemEval-2018 (Task 1) and SemEval-2019 (Task 3). Some tasks were focusing on classifying utterance into several categories of emotion BIBREF32 . However, there is also a task which trying to predict the emotion intensities contained in the text BIBREF33 . In the early development of emotion classifier, most of the studies proposed to use traditional machine-learning approach. However, the neural-based approach is able to gain better performance, which leads more scholars to exploit it to deal with this task. In chatbot, the system will generate several responses based on several emotion categories. Then the system will respond with the most appropriate emotion based on emotion detected on posted utterance by emotion classifier. Based on Table TABREF10 , studies have different emotion categories based on their focus and objective in building chatbots.
Resource for Building EAC
In this section, we try to investigate the available resources in building EAC. As other artificial intelligent agents, building chatbot also needs a dataset to be learned, to be able to produce a meaningful conversation as a human-like agent. Therefore, some studies propose dataset which contains textual conversation annotated by different emotion categories. Table TABREF12 summarizes the available dataset that found in recent years. We categorize the dataset based on the language, source of the data, and further description which contains some information such as annotation approach, size of instances, and emotion labels. All datasets were proposed during 2017 and 2018, started by dataset provided by NLPCC 2017 Shared Task on Emotion Generation Challenge organizers. This dataset gathered from Sina Weibo social media , so it consists of social conversation in Chinese. Based on our study, all of the datasets that we discover are only available in two languages, English and Chinese. However, the source of these datasets is very diverse such as social media (Twitter, Sina Weibo, and Facebook Message), online content, and human writing through crowdsourcing scenario. Our investigation found that every dataset use different set of emotion label depends on its focus and objective in building the chatbot.
As we mentioned in the previous section, that emotion classifier is also an integral part of the emotionally-aware chatbot. In building emotion classifier, there are also available several affective resources, which already widely used in the emotion classification task. Table TABREF16 shows the available affective resources we discovered, range from old resources such as LIWC, ANEW, and DAL, until the modern version such as DepecheMood and EmoWordNet. Based on some prior studies, emotion can be classified into two fundamental viewpoints, including discrete categories and dimensional models. Meanwhile, this feature view emotion as a discrete category which differentiates emotion into several primary emotion. The most popular was proposed by BIBREF37 that differentiate emotion into six categories including anger, disgust, fear, happiness, sadness, and surprise. Three different resources will be used to get emotion contained in the tweet, including EmoLex, EmoSenticNet, LIWC, DepecheMood, and EmoWordNet. Dimensional model is another viewpoint of emotion that define emotion according to one or more dimension. Based on the dimensional approach, emotion is a coincidence value on some different strategic dimensions. We will use two various lexical resources to get the information about the emotional dimension of each tweet, including Dictionary of Affect in Language (DAL), ANEW, and NRC VAD.
Evaluating EAC
We characterize the evaluation of Emotionally-Aware Chatbot into two different parts, qualitative and quantitative assessment. Qualitative assessment will focus on assessing the functionality of the software, while quantitative more focus on measure the chatbots' performance with a number.
Qualitative Assessment
Based on our investigation of several previous studies, we found that most of the works utilized ISO 9241 to assess chatbots' quality by focusing on the usability aspect. This aspect can be grouped into three focuses, including efficiency, effectiveness, and satisfaction, concerning systems' performance to achieve the specified goals. Here we will explain every focus based on several categories and quality attributes.
Efficiency aspect focuses on several categories, including robustness to manipulation and unexpected input BIBREF55 . Another study tries to asses the chatbots' ability to control damage and inappropriate utterance BIBREF56 .
Effectiveness aspect covers two different categories, functionality and humanity. In functionality point of view, a study by BIBREF57 propose to asses how a chatbot can interpret command accurately and provide its status report. Other functionalities, such as chatbots' ability to execute the task as requested, the output linguistic accuracy, and ease of use suggested being assessed BIBREF58 . Meanwhile, from the human aspect, most of the studies suggest each conversational machine should pass Turing test BIBREF21 . Other prominent abilities that chatbot needs to be mastered can respond to specific questions and able to maintain themed discussion.
Satisfaction aspect has three categories, including affect, ethics and behaviour, and accessibility. Affect is the most suitable assessment categories for EAC. This category asses several quality aspects such as, chatbots' ability to convey personality, give conversational cues, provide emotional information through tone, inflexion, and expressivity, entertain and/or enable the participant to enjoy the interaction and also read and respond to moods of human participant BIBREF59 . Ethic and behaviour category focuses on how a chatbot can protect and respect privacy BIBREF57 . Other quality aspects, including sensitivity to safety and social concerns and trustworthiness BIBREF60 . The last categories are accessibility, which the main quality aspect focus to assess the chatbot ability to detect meaning or intent and, also responds to social cues .
Quantitative Assessment
In automatic evaluation, some studies focus on evaluating the system at emotion level BIBREF15 , BIBREF28 . Therefore, some common metrics such as precision, recall, and accuracy are used to measure system performance, compared to the gold label. This evaluation is similar to emotion classification tasks such as previous SemEval 2018 BIBREF32 and SemEval 2019 . Other studies also proposed to use perplexity to evaluate the model at the content level (to determine whether the content is relevant and grammatical) BIBREF14 , BIBREF39 , BIBREF28 . This evaluation metric is widely used to evaluate dialogue-based systems which rely on probabilistic approach BIBREF61 . Another work by BIBREF14 used BLEU to evaluate the machine response and compare against the gold response (the actual response), although using BLEU to measure conversation generation task is not recommended by BIBREF62 due to its low correlation with human judgment.
This evaluation involves human judgement to measure the chatbots' performance, based on several criteria. BIBREF15 used three annotators to rate chatbots' response in two criteria, content (scale 0,1,2) and emotion (scale 0,1). Content is focused on measuring whether the response is natural acceptable and could plausible produced by a human. This metric measurement is already adopted and recommended by researchers and conversation challenging tasks, as proposed in BIBREF38 . Meanwhile, emotion is defined as whether the emotion expression contained in the response agrees with the given gold emotion category. Similarly, BIBREF28 used four annotators to score the response based on consistency, logic and emotion. Consistency measures the fluency and grammatical aspect of the response. Logic measures the degree whether the post and response logically match. Emotion measures the response, whether it contains the appropriate emotion. All of these aspects were measured by three scales 0, 1, and 2. Meanwhile, BIBREF39 proposed naturalness and emotion impact as criteria to evaluate the chatbots' response. Naturalness evaluates whether the response is intelligible, logically follows the context of the conversation, and acceptable as a human response, while emotion impact measures whether the response elicits a positive emotional or triggers an emotionally-positive dialogue, since their study focus only on positive emotion. Another study by BIBREF14 uses crowdsourcing to gather human judgement based on three aspects of performance including empathy/sympathy - did the responses show understanding of the feelings of the person talking about their experience?; relevance - did the responses seem appropriate to the conversation? Were they on-topic?; and fluency - could you understand the responses? Did the language seem accurate?. All of these aspects recorded with three different response, i.e., (1: not at all, 3: somewhat, 5: very much) from around 100 different annotators. After getting all of the human judgement with different criteria, some of these studies used a t-test to get the statistical significance BIBREF28 , BIBREF39 , while some other used inter-annotator agreement measurement such as Fleiss Kappa BIBREF15 , BIBREF14 . Based on these evaluations, they can compare their system performance with baseline or any other state of the art systems.
Related Work
There is some work which tried to provide a full story of chatbot development both in industries and academic environment. However, as far as my knowledge, there is still no study focused on summarizing the development of chatbot which taking into account the emotional aspect, that getting more attention in recent years. BIBREF63 provides a long history of chatbots technology development. They also described several uses of chatbots' in some practical domains such as tools entertainment, tools to learn and practice language, information retrieval tools, and assistance for e-commerce of other business activities. Then, BIBREF64 reviewed the development of chatbots from rudimentary model to more advanced intelligent system. They summarized several techniques used to develop chatbots from early development until recent years. Recently, BIBREF65 provide a more systematic shape to review some previous works on chatbots' development. They classify chatbots into two main categories based on goals, including task-oriented chatbots and non-task oriented chatbot. They also classify chatbot based on its development technique into three main categories, including rule-based, retrieval-based, and generative-based approach. Furthermore, they also summarized the detailed technique on these three main approaches.
Discussion and Conclusion
In this work, a systematic review of emotionally-aware chatbots is proposed. We focus on three main issues, including, how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance. The rise of EAC was started by Parry, which uses a simple rule-based approach. Now, most of EAC are built by using a neural-based approach, by exploiting emotion classifier to detect emotion contained in the text. In the modern era, the development of EAC gains more attention since Emotion Generation Challenge shared task on NLPCC 2017. In this era, most EAC is developed by adopting encoder-decoder architecture with sequence-to-sequence learning. Some variant of the recurrent neural network is used in the learning process, including long-short-term memory (LSTM) and gated recurrent unit (GRU). There are also some datasets available for developing EAC now. However, the datasets are only available in English and Chinese. These datasets are gathered from various sources, including social media, online website and manual construction by crowdsourcing. Overall, the difference between these datasets and the common datasets for building chatbot is the presence of an emotion label. In addition, we also investigate the available affective resources which usually use in the emotion classification task. In this part, we only focus on English resources and found several resources from the old one such as LIWC and Emolex to the new one, including DepecheMood and EmoWordNet. In the final part, we gather information about how to evaluate the performance of EAC, and we can classify the approach into two techniques, including qualitative and quantitative assessment. For qualitative assessment, most studies used ISO 9241, which covers several aspects such as efficiency, effectiveness, and satisfaction. While in quantitative analysis, two techniques can be used, including automatic evaluation (by using perplexity) and manual evaluation (involving human judgement). Overall, we can see that effort to humanize chatbots by incorporation affective aspect is becoming the hot topic now. We also predict that this development will continue by going into multilingual perspective since up to now every chatbot only focusing on one language. Also, we think that in the future the studies of humanizing chatbot are not only utilized emotion information but will also focus on a contextual-aware chatbot.

Question: How are EAC evaluated?
Answer: Qualitatively through efficiency, effectiveness and satisfaction aspects and quantitatively through metrics such as precision, recall, accuracy, BLEU score and even human judgement.

Question: What are the currently available datasets for EAC?
Answer: EMPATHETICDIALOGUES dataset, a dataset containing 1.5 million Twitter conversation, gathered by using Twitter API from customer care account of 62 brands across several industries, SEMAINE corpus BIBREF30

Question: What are the research questions posed in the paper regarding EAC studies?
Answer: how to incorporate affective information into chatbots, what are resources that available and can be used to build EAC, and how to evaluate EAC performance