Empathy-driven A rabic Conversational Chatbot (2020)

Abstract: Conversational models have witnessed a significant research interest in the last few years with the advancements in sequence generation models. A challenging aspect in developing human-like conversational models is enabling the sense of empathy in bots, making them infer emotions from the person they are interacting with. By learning to develop empathy, chatbot models are able to provide human-like, empathetic responses, thus making the human-machine interaction close to human-human interaction. Recent advances in English use complex encoder-decoder language models that require large amounts of empathetic conversational data. However, research has not produced empathetic bots for Arabic. Furthermore, there is a lack of Arabic conversational data labeled with empathy. To address these challenges, we create an Arabic conversational dataset that comprises empathetic responses. However, the dataset is not large enough to develop very complex encoder-decoder models. To address the limitation of data scale, we propose a special encoder-decoder composed of a Long Short-Term Memory (LSTM) Sequence-to-Sequence (Seq2Seq) with Attention. The experiments showed success of our proposed empathy-driven Arabic chatbot in generating empathetic responses with a perplexity of 38.6, an empathy score of 3.7, and a fluency score of 3.92.

## introduction empathy is described as the ability of recognizing others' state of mind and making sense of their feelings. empathetic behavior is provoked after being exposed to others' emotional states @xcite . empathy is an innate capacity in most human beings, and is also described as a responsive and spontaneous act of copying of an implied feeling. empathy in humans triggers a sense of concern for others, leading to appropriate emotional reactions that impose a positive effect on interacting individuals. for instance, empathetic behavior is applicable to situations such as acknowledging others' pain, showing interest, gratitude, being supportive, or providing encouragement. building chatbots that can exhibit empathetic behavior becomes a necessary step towards building emotionally intelligent conversational systems @xcite . such a trait allows conversational systems to be perceived as genuine and warm by users, rather than oblivious and boorish. this characteristic is highly desirable since it could boost user satisfaction in various chatbot applications. hence, the objective of this work is to develop an empathetic chatbot for the arabic language. an example of the desired chatbot with empathetic behavior is illustrated in fig. 1 that shows the difference between empathetic and unempathetic responses. for instance, in fig 1a the chatbot infers a feeling of sadness in the user's text and provides an empathetic response to comfort the user, while in fig 1b the chatbot infers a feeling of pride in the user's statement and thus congratulates them. recent advances in artificial intelligence (ai) and natural language processing (nlp) have made the development of such systems possible. specifically, the introduction of sequence-to-sequence (seq2seq) models and transformer networks have improved performance significantly. empathetic chatbots have grabbed interest in recent years with many sequence architectures proposed to generate empathetic responses given a user's emotional statement @xcite . the recent attempts in building empathetic chatbots for english have focused on training sequence generation models on a dataset of empathetic conversations @xcite , where models are trained on input/output sequences of speaker statements and their corresponding empathetic responses. the dataset used for english is based on empathetic conversations between a speaker and a listener. the speaker describes a situation he previously experienced, while the listener infers the emotional state of the speaker and provides a suitable empathetic response. hence, by training the model on such samples of speaker statements and listener empathetic responses, the chatbot learns to develop a sense of empathy and provides suitable responses to any given context. despite the various works presented in the literature on empathetic chatbots for english @xcite @xcite , no work has previously addressed the problem of building such a model for the arabic language. this is mainly due to the difficulties that arise when dealing with a morphologically rich language such as arabic. another important reason is the scarcity of resources available for arabic compared with the english language, including corpora, tools, and pre-trained models. to address these challenges, we propose a neural sequence generation model based on seq2seq with lstm units combined with attention. we select this model over state-of-the-art transformer models due to their low computational cost and suitability for smaller datasets that are more feasible for arabic. additionally, we create a corpus for empathetic conversations in arabic by translating datasets available for english. the developed model successfully exhibited empathetic behavior and provided emotional responses to the input of users in arabic. the rest of this paper is organized as follows: section 2 summarizes the recent literature on empathetic chatbots. section 3 describes the arabic dataset created for empathetic chatbots and presents the proposed seq2seq model. experiments and results are presented in section 4. concluding remarks and future directions follow in section 5. ## related work in this section, we start by reviewing the recent literature on empathetic chatbots for the english language, including datasets and models used. we then review the current state of arabic chatbots and highlight the existing gaps. ## proposed method in this section, we present the proposed model for arabic empathy-driven conversational bots and the dataset we created. we start by describing the details of the proposed encoder-decoder model in subsection 3.1. we then present the dataset created for arabic empathetic chatbots in subsection 3.2, which we used to train our proposed model. ## experiments and results in this section, we start by defining the experimental setup in subsection 4.1, including how the dataset is split and what model configurations is trained. we analyze the results in subsection 4.2. we then present the results of the human evaluations in subsection 4.3 and provide a discussion on the strengths and shortcoming of the proposed model. ## conclusion in this paper, we proposed the first model for arabic empathetic conversational bots and a dataset of empathetic conversations in arabic. our proposed model is a seq2seq model with lstm units combined with attention. the dataset created was translated from the empatheticdialogues dataset available in english and showed an accuracy of 96% on a sample of the data, which was deemed as sufficient for the purpose of training conversational bots. upon experimenting with several model configurations, the proposed model with an embedding dimension of 500 reached state of the art performance for arabic with a ppl of 38.6 and a bleu score of 0.5. human evaluation of the generated responses also validated the success of the proposed model, which reached an average empathy score of 3.7 and an average fluency score of 3.92. our results were promising and showed the ability of the proposed model in inferring speaker emotions and generating empathetic responses. however, the model showed average score in relevance indicating that the response may sometimes go off topic. the limitations of this model are mainly due to the small size of the dataset created. therefore, our future directions include creating a large conversational dataset for the arabic language. the larger dataset will also enable the exploration of complex sequence generation models such as transformers and pre-trained models.