Forecasting Implicit Emotions Elicited in Conversations (2024)

Abstract: This paper aims to forecast the implicit emotion elicited in the dialogue partner by a textual input utterance. Forecasting the interlocutor’s emotion is beneficial for natural language generation in dialogue systems to avoid generating utterances that make the users uncomfortable. Previous studies forecast the emotion conveyed in the interlocutor’s response, assuming it will explicitly reflect their elicited emotion. However, true emotions are not always expressed verbally. We propose a new task to directly forecast the implicit emotion elicited by an input utterance, which does not rely on this assumption. We compare this task with related ones to investigate the impact of dialogue history and one’s own utterance on predicting explicit and implicit emotions. Our result highlights the importance of dialogue history for predicting implicit emotions. It also reveals that, unlike explicit emotions, implicit emotions show limited improvement in predictive performance with one’s own utterance, and that they are more difficult to predict than explicit emotions. We find that even a large language model (LLM) struggles to forecast implicit emotions accurately.

## introduction dialogue system is a key application of natural language generation. for dialogue systems, forecasting user reactions to generated utterances is beneficial for preventing potentially offensive responses. in this research, we introduce the task of forecasting the implicit emotion elicited in the dialogue partner by a textual input utterance. several previous studies @xcite @xcite forecast the emotion of a dialogue partner by using speaker emotion datasets. the emotion labels in these datasets represent the emotions expressed in utterances, which means they assumed the emotion elicited in the interlocutor will explicitly be conveyed in their response. however, this does i wonder if we can make it. well, we'll be alright. yeah, it really is. this project is quite difficult, isn't it? plutchik's wheel of emotions @xcite . in this conversation, while b feels apprehension because of a's anxious utterance, "i wonder if we can make it.", b expresses optimism in his utterance to encourage himself. ## related work some previous studies have attempted the forecasting task, which is to predict the dialogue partner's emotion. they incorporated commonsense knowledge @xcite or emotional persistence and contagiousness @xcite in addition to dialogue history @xcite . their task differs from ours as they employed speaker emotion datasets for training and evaluation. listener emotion datasets are used by two studies. the first one @xcite , which created the memor dataset, predicted both the speakers' explicit emotions and the listeners' implicit emotions based on multimodal and personality information. the results suggest that predicting listeners' emotions is more difficult than predicting speakers' emotions. this work differs from ours as it used the subsequent context, which is unavailable in dialogue systems. the other study @xcite , which created the reconstructed memor dataset, built a positive emotion elicitation dialogue system. memor was reconstructed so that all the emotion labels could be inferred from the textual information alone. the dataset was used to train a latent variable to control the emotional tone of utterances. instead, we train a model to forecast implicit emotions directly. implementing such a model in dialogue systems will enhance their interpretability. ## emotion classification tasks we focus on the task of forecasting the implicit emotion elicited by an utterance in its listener and compare it to three related tasks. the four tasks are divided into explicit and implicit emotion predictions, and further into recognition and forecasting. here, the speaker emotion refers to the emotion explicitly expressed in an utterance, and the listener emotion refers to the implicit emotion elicited by an utterance. figure 1 and ## results figure 2 displays the macro-f1 and f1 w/o neutral scores of distilroberta across the four tasks. the left figures show the results using all available training data for each task, while the right ones show the results using a standardized 4,767 training samples for all the tasks. each score point and its corresponding error bar represent the average and standard error of five trials with different random seeds for train data selection. overall, the results with dialogue history outperform those without it, especially for implicit tasks. this indicates that the context is important in predicting implicit emotions. as for iefc with 4,767 training samples, the last utterance setting yielded better results than the full-history setting. this might be because the elicited implicit emotion is greatly influenced by the person's last utterance (e.g., b's utterance "yeah, it really is." in figure 1 ), and can be confused by earlier dialogue history 2 https://huggingface.co/meta-llama/ llama-2-13b-hf (e.g., "this project is quite difficult, isn't it?" in figure 1 ). ## conclusion we proposed a new task to forecast the implicit emotion elicited in the listener by an input utterance, and analyzed its difficulty by comparing it with three related tasks. the analysis suggests three points: (1) dialogue history is important for predicting implicit emotions, (2) unlike explicit emotions, implicit emotions show limited improvement in predictive performance with one's own utterance, (3) implicit emotions are more challenging to predict than explicit ones. additionally, we fine-tuned llama 2 for the new task and found it struggles to accurately forecast elicited implicit emotions. as future work to improve its performance, possible directions include applying prompt engineering techniques or using other large language models. incorporating personality information @xcite or commonsense knowledge @xcite is also a promising approach. personalities will be particularly important for this task, since the emotion elicited in the listener by an utterance is likely to vary with the personality of the listener @xcite . further, this task can be extended to multi-party conversations and situations with multimodal information.