Knowledge Bridging for Empathetic Dialogue Generation

Abstract

Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history. To address the above problems, we propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation. We first enrich the dialogue history by jointly interacting with external knowledge and construct an emotional context graph. Then we learn emotional context representations from the knowledge-enriched emotional context graph and distill emotional signals, which are the prerequisites to predicate emotions expressed in responses. Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph. Extensive experiments conducted on a benchmark dataset verify the effectiveness of the proposed method. In addition, we find the performance of our method can be further improved by integrating with a pre-trained model that works orthogonally.

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

“…Instead of giving a predefined emotion label, empathetic conversation involves cognitive and affective empathy (Davis 1983;Zheng et al 2021) and aims to fully understand the interlocutor's situation and feelings and respond empathically (Keskin 2014). 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).…”

“…As the concept knowledge, ConceptNet (Speer, Chin, and Havasi 2017) provides word-level human knowledge and is widely used in various NLP tasks (Zhang et al 2020;Zhong et al 2021). Following Li et al (2022), we use NRC VAD (Mohammad 2018) to assign emotion intensity to concepts in ConceptNet (processing details are in Li et al (2022)) severed to extract the contextual emotional state manifested in the dialogue context, and align it with contextual cognition.…”

“…, w n ], where n is the number of all the tokens in the utterances in X. Following Li et al (2022), we use ConceptNet to infer the related concepts for each token w i ∈ T , among which only the the top N emotional concepts (according to the emotion intensity ω(c)) are used for constructing G EC . Subsequently, the vertices V EC in the heterogeneous emotional concept graph G EC contains a [CLS] token, the dialogue context tokens T , and the above obtained emotional concepts.…”

“…Although both aspects are considered to generate empathetic responses, existing works still remain issues. 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.…”

CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation

“…Three models that merely consider the affective aspect of the empathy are selected. They are: In addition, four models that considers both the affection and cognition of empathy are selected: KEMP 7 (Li et al, 2022) This model leverages external commonsense knowledge and emotional lexicon to understand and express emotion for empathetic response generation.…”