CEM: Commonsense-Aware Empathetic Response Generation

Abstract

A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user’s emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user’s emotion, cognitive understanding of the user’s situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user’s situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EMPATHETICDIALOGUES, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses. Our code is available at https://github.com/Sahandfer/CEM.

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation

“…On the one hand, most existing works only focus on the affective aspect of empathy and make efforts to detect contextual emotion (Rashkin et al 2019;Lin et al 2019;Majumder et al 2020;Li et al 2020Li et al , 2022 while ignoring the cognitive aspect. On the other hand, some research leverage commonsense as cognition to refine empathetic considerations (Sabour, Zheng, and Huang 2022). However, the relatively independent modeling between the two aspects (i.e., cognition and affection) violates their interrelated characteristics.…”

“…, where r is one of the four commonsense relations R = {xIntent, xNeed, xWant, xEffect}, similar to Sabour, Zheng, and Huang (2022). The idea is that human responses tend to inherit the above and transfer the topic.…”

“…Context Encoding Following previous works (Majumder et al 2020;Sabour, Zheng, and Huang 2022), we first concatenate all the utterances in the dialogue context X and prepend with a [CLS] token: [CLS] ⊕ x 1 ⊕ . .…”

“…Since k xReact i,j ∈ K xReact i is usually an emotion word (e.g., happy, sad), we concatenate K xReact i and feed it into the Transformer-based encoder (reaction encoder) to get the representation of the emotional reaction H er i . Similar to (Majumder et al 2020) and (Sabour, Zheng, and Huang 2022), we use the average pooling to represent the reaction sequence, i.e., h er i = Average (H er i ). To avoid overalignment of out-of-context emotional reaction with cognition, we inject contextual information into the representation of reaction.…”

“…On the one hand, most works (Rashkin et al 2019;Lin et al 2019;Majumder et al 2020;Li et al 2020Li et al , 2022 rely solely on the affective aspect to detect and utilize contextual emotion for enhancing empathy in response generation. On the other hand, previous researches (Zheng et al 2021;Sabour, Zheng, and Huang 2022) model cognition and affection as two relatively independent aspects to improve the understanding and expression of empathy. However, the birth of empathetic responses in human conversation often results from conscious alignment and interaction between cognition and affection of empathy (Westbrook, Kennerley, and Kirk 2011).…”