Empathy Intent Drives Empathy Detection (2023)

Abstract: Empathy plays an important role in the human dialogue. Detecting the empathetic direction expressed by the user is necessary for empathetic dialogue systems because it is highly relevant to understanding the user’s needs. Several studies have shown that empathy intent information improves the ability to response capacity of empathetic dialogue. However, the interaction between empathy detection and empathy intent recognition has not been explored. To this end, we invite 3 experts to manually annotate the healthy empathy detection datasets IEMPATHIZE and TwittEmp with 8 empathy intent labels, and perform joint training for the two tasks. Empirical study has shown that the introduction of empathy intent recognition task can improve the accuracy of empathy detection task, and we analyze possible reasons for this improvement. To make joint training of the two tasks more challenging, we propose a novel framework, Cascaded Label Signal Network, which uses the cascaded interactive attention module and the label signal enhancement module to capture feature exchange information between empathy and empathy intent representations. Experimental results show that our framework outperforms all baselines under both settings on the two datasets.

## introduction empathy is essential in human social interaction. in the process of human dialogue, empathy enables listeners to establish rapport with speakers by understanding their emotional and cognitive states, arousing their interest, and comforting them @xcite . therefore, it is worthwhile to detect the empathetic direction of dialogue utterances. in recent years, researchers have studied empathy detection in various fields, such as mental health support @xcite interaction empathy decoder encoder empathy representation empathy decoder empathy intent decoder encoder mixed representation mixed representation encoder empathy representation empathy intent representation empathy decoder empathy intent decoder jurgens, 2020), empathetic expression understanding in newswire @xcite , medical and healthcare @xcite @xcite , human-computer interaction @xcite @xcite , etc. currently, millions of people seek psychological support by expressing their emotions in online health communities and look forward to receiving support from peers who may have had similar experiences and can understand their feelings. therefore, some researchers have performed studies on the direction of empathy expression in people's utterances. for example, the utterance: "i lost my mom to cancer in april and just miss her so much. there are so many pieces to work on and i find it so hard to work on bc my grief is so strong". the model needs to detect the direction of empathy expressed by the user, 'seek'. @xcite provided an online healthy dataset and a baseline for empathy detection (ed), but their methods are monotonous, as shown in figure 1 (a). they did not consider that humans have potential empa-thy intent information while expressing empathy. @xcite learned the distribution of potential empathy intent and then combined implicit and explicit representations of empathy intent to generate responses with empathy intent. however, they do not explore the interaction between empathy and empathy intent representations. in this paper, we invite 3 experts to manually annotate the empathy intent of each utterance in two empathy detection datasets iempathize (hosseini and caragea, 2021b) and twittemp @xcite according to the lexicon of empathy intent example utterance provided by @xcite . on this basis, we use a simple joint training method to test the feasibility of empathy intent recognition (eic) as an auxiliary task, as shown in figure 1 (b ). the experimental results show that the joint training of ed and eic leads to higher accuracy of ed, and we find that the categories of ed are easier to distinguish after the introduction of eic due to the obvious correspondence between empathy and empathy intent labels. to make joint training tasks more challenging, we propose a novel framework, called cascaded label signal network (clsn). first, it uses the bert (kenton and toutanova, 2019) model to obtain the semantic features of utterances and extracts different representations through two linear layers. then, the cascaded interactive attention module is used to implement feature interaction and control knowledge flow. finally, the label signal enhancement module is used to further extract interactive features from the label information of the two tasks and feed them to different decoders to complete ed and eic. we model the interaction information flow between both tasks, as shown in figure 1(c ). overall, our contributions are as follows: (1) we invite 3 experts to manually annotate empathy intent labels on the ed datasets iempathize and twittemp, provide the necessary datasets for the joint ed and eic study; (2) to the best of our knowledge, we are the first to attempt joint training of the ed and eic tasks to improve the accuracy of the ed task. we also explore the possible reasons for the accuracy improvement; (3) to make joint training of the two tasks more challenging, we propose a novel framework, clsn, which explicitly controls the knowledge transfer between the two tasks. experimental results show that our framework outperforms all baselines under both settings in the two datasets. ## empirical study in this section, we introduce eic as an auxiliary task in the ed task to verify its effectiveness. in addition, we explore the reasons for the influence of the eic task on ed. ## methodology in this section, we provide a detailed description of the clsn, which can effectively model the interaction between empathy and empathy intent representations. it consists of an encoding layer ( §4.1), a cascaded interactive attention module ( §4.2), a la-bel signal enhancement module ( §4.3), and two separate classification decoders ( §4.4). an overview of our framework is shown in figure 5 . empathy decoder intent decoder e0 in em i0 ... ... label signal cascaded interactive attention module label signal enhancement module bert encoder vec vec kec kec qel qel vic vic kic kic qil qil label enhancement feature label enhancement feature u1 u2 us … u3 u4 u5 qe qe ke ke ve ve qi qi ki ki vi vi qi qi qe qe ke-i ke-i ve-i ve-i ki-e ki-e vi-e vi-e empathy decoder intent decoder e0 in em i0 ... ... label signal cascaded interactive attention module label signal enhancement module bert encoder vec kec qel vic kic qil label enhancement feature u1 u2 us … u3 u4 u5 qe ke ve qi ki vi qi qe ke-i ve-i ki-e vi-e ## conclusion in this paper, to investigate whether the performance of the ed task can be improved by the introduction of the eic task, we invite 3 experts to manually label the empathy intent labels on two empathy detection datasets, iempathize and twit-temp, as datasets for joint training of ed and eic. the empirical study shows that the introduction of eic task is effective in improving the accuracy of the ed task, and we also explore possible reasons for this improvement. in addition, we propose the clsn, which explicitly models the information flow interaction between the two representations through a cascaded interactive attention module and a labeled signal enhancement module. experimental results show that the clsn achieves better accuracy than all baselines under both settings in the two datasets.