i E val: Interactive Evaluation Framework for Open-Domain Empathetic Chatbots (2022)

Abstract: Building an empathetic chatbot is an important objective in dialog generation research, with evaluation being one of the most challenging parts. By empathy, we mean the ability to understand and relate to the speakers’ emotions, and respond to them appropriately. Human evaluation has been considered as the current standard for measuring the performance of open-domain empathetic chatbots. However, existing evaluation procedures suffer from a number of limitations we try to address in our current work. In this paper, we describe iEval, a novel interactive evaluation framework where the person chatting with the bots also rates them on different conversational aspects, as well as ranking them, resulting in greater consistency of the scores. We use iEval to benchmark several state-of-the-art empathetic chatbots, allowing us to discover some intricate details in their performance in different emotional contexts. Based on these results, we present key implications for further improvement of such chatbots. To facilitate other researchers using the iEval framework, we will release our dataset consisting of collected chat logs and human scores.

## introduction development of open-domain chatbots endowed with social and emotional intelligence is a crucial task in natural language research @xcite . empathetic chatbots are expected to engage in a conversation with the users and demonstrate understanding and appropriate handling of users' feelings. while many strategies for generating empathetic responses have been described, there is still little consensus on their evaluation. for dialog generation, automatic metrics do not show consistency in correlations with human judgement @xcite , leading to their limited adoption. therefore, most of existing works rely on human evaluation. it may happen in either static or interactive setting @xcite . in the former case, a human judge rates chatbot's responses, generated from a fixed set of contexts. in the latter case, dialogs for evaluation are collected as humans' multi-turn chats with the model. recently, two comprehensive approaches based on interactive multi-turn human evaluation were proposed. @xcite described a metric called sensibleness and specificity average, which measures these two aspects of chatbot's responses. human judges give likert-type scores to each chatbot's turn in a dialog, which are further averaged to obtain a final score. as likert-type scores may exhibit differing bias and variance per annotator, associated with the lack of sensitivity, @xcite suggested an alternative evaluation strategy based on pairwise comparisons. according to their method, human judges indicate their preference of one chatbot over another by comparing two dialog logs with these chatbots. this procedure is more robust, but become very costly when the number of compared models goes up. both of these approaches differentiate humans who interact with the models and humans who judge them. they probably opt for this design choice due to such considerations as workers' fatigue. however, according to findings in cognitive psychology, our emotional experiences are highly subjective. @xcite points out that only the experiencers can reveal the full complexity of emotions that they feel. for example, if a client complains about a hotel room being too cold, a third-party observer might underestimate the gravity of the issue, especially if he enjoys indoor coolness. this fact argues for the necessity of a new evaluation approach of chatbots, which would ensure that both emotional interaction and evaluation of a chatbot are accomplished by the same human actor. to help these humans share their emotional experiences, asking them to role-play a relatable scenario is a frequently used procedure in social sciences @xcite . in this work, we introduce ieval, an interactive evaluation framework for open-domain empathetic chatbots, which mitigates the issue of separating an experiencer and an evaluator. to combine the benefits of likert scales, allowing to evaluate many chatbots in a single stretch of time, and pairwise comparisons, offering greater reliability and cross-experiment robustness, we propose a novel ranking-based approach. according to ieval, a human first converses with all chatbots, having all chats grounded in an emotional scenario (figure 1 (a)). then, the same human ranks the models by dragging-and-dropping them into corresponding categories (figure 1 (b) ). our experiments demonstrate that ieval can reveal subtle but significant differences in chatbots' performance across emotional contexts. overall, our contributions include the following. 1) we describe a new evaluation framework to measure chatbots' abilities to respond appropriately in sensitive contexts. 2) we demonstrate a rigorous procedure for preparing grounding scenarios for the given evaluation task. 3) we benchmark several state-of-the-art empathetic chatbots, which have never been compared before. 4) based on the analysis of the benchmark results, we discuss implications for the future development of empathetic chatbots. 5) finally, we release the data from our experiments to facilitate future research endeavors. ## related work most works focusing on the development of empathetic chatbots couple automatic evaluation with human judgement. automatic metrics usually include perplexity, approximating the model's language modeling ability @xcite @xcite , and may incorporate other scores, depending on the specific focus of the work. some frequently used examples are bleu score @xcite , diversity metrics @xcite , and f-1 score or accuracy of emotion detection @xcite @xcite . since the appropriateness of automatic metrics for open-domain dialog is still ambiguous, all works de facto rely on human judgement. most commonly, researchers employ single-turn static evaluation, where a fixed emotionally-colored context is shown to a judge along with the responses generated by different chatbots. the judges are asked to rate how empathetically appropriate the responses are, and the assessment may come either as likert-type scores @xcite @xcite or ranking @xcite . although this approach is widespread due to the ease of implementation, it fails to capture issues emerging in multi-turn chats, such as repetitiveness or deterioration of semantic coherence in long-range contexts @xcite . few works that focus on integrating empathetic abilities into chatbots started adopting interactive evaluations. @xcite employed acute-eval @xcite framework based on pairwise comparisons to assess engaginess and humanness of their models. @xcite defined their own evaluation protocol to collect likerttype scores for a series of dimensions measuring chatbot's performance. however, in both of these studies, the evaluated data points were open-ended chats that began with a generic greeting. based on the provided examples of conversations, these exchanges generally developed as light small-talk, maintaining neutral or positive sentiments. therefore, it remains unclear how well the collected scores reflect empathetic abilities of the chatbots, which should ideally succeed over a range of emotions. our framework addresses this limitation by grounding the chats in diverse emotional scenarios. ## method: ieval to compare empathetic abilities of several chatbots, ieval suggests that at first a human makes an emotionally-grounded conversion with each bot in a randomized order. if necessary, fine-grained likert-type assessments of specific chatbot's performance aspects may be collected after each conversation. as the next step, the same human is asked to rank the chatbots according to her experience with them. an example of this flow is given in figure 1 . finally, appropriate statistical instruments should be applied to compare the chatbots. ## experiment to demonstrate how ieval works in practice, we apply the framework to benchmark several stateof-the-art empathetic chatbots, which have never been compared against each other in an interactive setting. the details and analysis are outlined below. ## analysis of results below, we describe the eventual ranking of the models and consider the aspects that likely explain the observed results. ## limitations in our work, we applied ieval framework to benchmark four empathetic agents. we did not compare them against human-human interaction, as synchronizing two crowdworkers for conducting several chats between each other entails more logistical difficulties. more importantly, we were mainly interested in measuring how existing chatbots address users' emotional needs, rather than checking if they are indistinguishable from human interlocutors. our results show that bigger models rank higher in the evaluation task. it raises the subsequent question about to what extent the proposed framework measures differences in models' empathetic abilities compared to their underlying language model performances. we believe that ieval is an effective framework for evaluating chatbots' empathy as it succeeded in registering intricate differences in the performances of meed and plain, two models of comparable sizes and pre-training pipelines, as well as distinguishing the performance of blender in emotional contexts of different polarity. to further disentangle the role of language modeling and empathetic abilities, one can consider running the ieval evaluation experiment to compare equal-size models with and without fine-tuning for empathetic response generation (e.g., blender, which was only pre-trained on reddit, and blender, which was further fine-tuned on the empatheticdialogues dataset). however, this was not the main objective of our study and we leave it for future work. finally, we propose to use ranking as a way of expressing the appraisals of the chatbots, as it affords advantages of both likert scales and pairwise comparisons. ranking may be less robust for comparing results across experiments with mismatched sets of chatbots. applying rank aggregation techniques can be useful to tackle such cases @xcite . ## conclusion our paper introduced ieval, a novel evaluation framework for open-domain chatbots that can detect humans' personal perceptions of social interaction, manifesting in emotional dialogs. we used ieval to benchmark four recent empathetic chatbots. further analysis revealed several limitations in empathetic response generation approaches of these models, which came out due to their uneven abilities in handling positive and negative conversational scenarios. based on our findings, we formulated implications informing future efforts in the development and evaluation of such chatbots. we also publicly release the data from our experiment to expedite future research in these directions.