For a conversation to help and support, speakers should maintain an “effect-effort” tradeoff. As outlined in the gist of “Cognitive Relevance Principle”, helpful speakers should optimize the “cognitive relevance” through maximizing the “cognitive effects” and minimizing the “processing effort” imposed on listeners. Although preference learning methods have given rise a boon of studies in pursuit of“effect-optimization”, none have delved into the critical “effort-optimiazation” to fully cultivate the awareness of “optimal relevance” into thecognition of conversation agents. To address this gap, we integrate the “Cognitive Relevance Principle” into emotional support agents in the environment of multi-turn conversation. The results demonstrate a significant and robust improvement against the baseline systems with respect to response quality, human-likedness and supportivenss. This study offers compelling evidence for the effectiveness of the “Relevance Principle” in generating human-like, helpful, and harmless emotional support conversations. The source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git

To provide effective support, it is essential for a skilled supporter to emotionally resonate with the help-seeker’s current emotional state. In conversational interactions, this emotional alignment is further influenced by the comforting strategies employed by the supporter. Different strategies guide the interlocutors to align their emotions in nuanced patterns. However, the incorporation of strategy into emotional alignment in the context of emotional support agents remains underexplored. To address this limitation, we propose an improved emotional support agent called Emstremo. Emstremo aims to achieve strategic control of emotional alignment by perceiving and responding to the user’s emotions. Our system’s state-of-the-art performance emphasizes the importance of integrating emotions and strategies in modeling conversations that provide emotional support.

Eye-tracking data in Chinese languages present unique challenges due to the non-alphabetic and unspaced nature of the Chinese writing systems. This paper introduces the first deeply-annotated joint Mandarin-Cantonese eye-tracking dataset, from which we achieve a unified eye-tracking prediction system for both language varieties. In addition to the commonly studied first fixation duration and the total fixation duration, this dataset also includes the second fixation duration, expressing fixation patterns that are more relevant to higher-level, structural processing. A basic comparison of the features and measurements in our dataset revealed variation between Mandarin and Cantonese on fixation patterns related to word class and word position. The test of feature usefulness suggested that traditional features are less powerful in predicting the second-pass fixation, to which the linear distance to root makes a leading contribution in Mandarin. In contrast, Cantonese eye-movement behavior relies more on word position and part of speech.