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Emotionally-Aware Chatbots: a Survey Conference’17, July 2017, Washington, DC, USA Emotionally-Aware Chatbots: A Survey Endang Wahyu Pamungkas pamungka@di.unito.it University of Turin Turin, Italy ABSTRACT latest years. Some works tried to model them into domain-specific Textual conversational agent or chatbots’ development gather tremen- tasks such as customer service [10, 18], and shopping assistance 1 dous traction from both academia and industries in recent years. [22]. Other works design a multi-purpose agents such as SIRI , 2 3 Nowadays, chatbots are widely used as an agent to communicate Amazon Alexa , and Google Assistance . This domain is a well- with a human in some services such as booking assistant, customer researched area in Human-Computer Interaction research commu- service, and also a personal partner. The biggest challenge in build- nity but still become a hot topic now. The main development fo- ing chatbot is to build a humanizing machine to improve user en- cus right now is to have an intelligent and humanizing machine to gagement. Some studies show that emotion is an important aspect have a better engagement when communicating with human [16]. to humanize machine, including chatbot. In this paper, we will pro- Having a better engagement will lead to higher user satisfaction, vide a systematic review of approaches in building an emotionally- which becomes the main objective from the industry perspective. aware chatbot (EAC). As far as our knowledge, there is still no In this study, we will only focus on textual conversational agent work focusing on this area. We propose three research question or chatbot, a conversational artificial intelligence which can con- regarding EAC studies. We start with the history and evolution of duct a textual communication with a human by exploiting sev- EAC, then several approaches to build EAC by previous studies, eral natural language processing techniques. There are several ap- and some available resources in building EAC. Based on our inves- proaches used to build a chatbot, start by using a simple rule-based tigation, we found that in the early development, EAC exploits a approach [1, 46] until more sophisticated one by using neural-based simple rule-based approach while now most of EAC use neural- technique [44, 47]. Nowadays, chatbots are mostly used as cus- based approach. We also notice that most of EAC contain emotion tomer service such as booking systems [38, 59], shopping assis- 4 classifier in their architecture, which utilize several available affec- tance [10] or just as conversational partner such as Endurance 5 tive resources. We also predict that the development of EAC will and Insomnobot . Therefore, there is a significant urgency to hu- continue to gain more and more attention from scholars, noted by manize chatbot for having a better user-engagement. Some works some recent studies propose new datasets for building EAC in var- were already proposed several approaches to improve chatbot’s ious languages. user-engagement, such as building a context-aware chatbot [52] and injecting personality into the machine [63]. Other works also CCS CONCEPTS try to incorporate affective computing to build emotionally-aware chatbots [18, 45, 65]. • Human-centered computing → Natural language interfaces; Some existing studies shows that adding emotion information • Computing methodologies → Natural language generation. into dialogue systems is able to improve user-satisfaction [43, 61]. KEYWORDS Emotion information contribute to a more positive interaction be- tween machine and human, which lead to reduce miscommunica- chatbot, conversational agent, affective computing, natural language tion [28]. Some previous studies also found that using affect infor- generation mation can help chatbot to understand users’ emotional state, in ACM Reference Format: order to generate better response [41]. Not only emotion, another arXiv:1906.09774v1 [cs.CL] 24 Jun 2019 Endang Wahyu Pamungkas. 2018. Emotionally-Aware Chatbots: A Survey. study also introduce the use of tones to improve satisfactory ser- In Proceedings of ACM Conference (Conference’17). ACM, New York, NY, vice. For instance, using empathetic tone is able to reduces user USA, 8 pages. https://doi.org/10.1145/1122445.1122456 stress and results in more engagement. [18] found that tones is an important aspect in building customer care chatbot. They dis- 1 INTRODUCTION cover eight different tones including anxious, frustrated, impolite, Conversational agents or dialogue systems development are gain- passionate, polite, sad, satisfied, and empathetic. ing more attention from both industry and academia [15, 17] in the In this paper, we will try to summarize some previous studies which focus on injecting emotion information into chatbots, on dis- Permission to make digital or hard copies of all or part of this work for personal or covering recent issues and barriers in building engaging emotionally- classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full cita- aware chatbots. Therefore, we propose some research questions to tion on the first page. Copyrights for components of this work owned by others than have a better problem definition: the author(s) must be honored. Abstracting with credit is permitted. To copy other- wise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. 1https://www.apple.com/es/siri/ Conference’17, July 2017, Washington, DC, USA 2https://developer.amazon.com/alexa © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. 3https://assistant.google.com/ ACM ISBN 978-1-4503-9999-9/18/06...$15.00 4https://bit.ly/2RGM40S https://doi.org/10.1145/1122445.1122456 5http://insomnobot3000.com/ Conference’17, July 2017, Washington, DC, USA Pamungkas. RQ1 How to incorporate emotion information in building an emotionally-judgement for deciding which response that elicits positive emo- aware chatbot? tion. Their dataset was used to develop a chatbot which captures RQ2 What are available resources that can be used in building human emotional states and elicits positive emotion during the emotionally-aware chatbots? conversation. RQ3 How to evaluate the performance of emotionally-aware chat- bots? 3 BUILDING EMOTIONALLY-AWARE This paper will be organized as follows: Section 2 introduces the CHATBOT (EAC) history of the relation between affective information with chatbots. As we mentioned before that emotion is an essential aspect of Section 3 outline some works which try to inject affective informa- building humanize chatbot. The rise of the emotionally-aware chat- tion into chatbots. Section 4 summarizes some affective resources bot is started by Parry [8] in early 1975. Now, most of EAC devel- which can be utilized to provide affective information. Then, Sec- opment exploits neural-based model. In this section, we will tryto tion 5 describes some evaluation metric that already applied in review previous works which focus on EAC development. Table 1 some previous works related to emotionally-aware chatbots. Last summarizes this information includes the objective and exploited Section 6 will conclude the rest of the paper and provide a predic- approach of each work. In early development, EAC is designed tion of future development in this research direction based on our by using a rule-based approach. However, in recent years mostly analysis. 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 [20]. Based on 2 HISTORY OF EMOTIONALLY-AWARE Table 1 this research line continues to gain massive attention from CHATBOT scholars in the latest years. The early development of chatbot was inspired by Turing test in Based on Table 1, we can see that most of all recent EAC was 1950 [57]. Eliza was the first publicly known chatbot, built by us- built by using encoder-decoder architecture with sequence-to-sequence ing simple hand-crafted script [58]. Parry [8] was another chatbot learning. These seq2seq learning models maximize the likelihood which successfully passed the Turing test. Similar to Eliza, Parry of response and are prepared to incorporate rich data to gener- still uses a rule-based approach but with a better understanding, ate an appropriate answer. Basic seq2seq architecture structured including the mental model that can stimulate emotion. Therefore, of two recurrent neural networks (RNNs), one as an encoder pro- Parry is the first chatbot which involving emotion in its develop- cessing the input and one as a decoder generating the response. ment. Also, worth to be mentioned is ALICE (Artificial Linguistic long short term memory (LSTM) or gated recurrent unit (GRU) Internet Computer Entity), a customizable chatbot by using Artifi- was the most dominant variant of RNNs which used to learn the cial Intelligence Markup Language (AIML). Therefore, ALICE still conversational dataset in these models. Some studies also tried to also use a rule-based approach by executing a pattern-matcher re- model this task as a reinforcement learning task, in order to get cursively to obtain the response. Then in May 2014, Microsoft in- more generic responses and let the chatbot able to achieve suc- troduced XiaoIce [66], an empathetic social chatbot which is able cessful long-term conversation. Attention mechanism was also in- 6 to recognize users’ emotional needs. XiaoIce can provide an en- troduced in this report . This mechanism will allow the decoder to gaging interpersonal communication by giving encouragement or focus only on some important parts in the input at every decoding other affective messages, so that can hold human attention during step. communication. Another vital part of building EAC is emotion classifier to de- Nowadays, most of chatbots technologies were built by using tect emotion contained in the text to produce a more meaningful neural-based approach. Emotional Chatting Machine (ECM) [65] response. Emotion detection is a well-established task in natural was the first works which exploiting deep learning approach in language processing research area.
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