Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes (2021)

Abstract: Empathy is a complex cognitive ability based on the reasoning of others’ affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other’s emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.

## introduction empathy is one of the hallmarks of social cognition. it is an intricate cognitive ability that requires high-level reasoning on other's affective states. the intensity of expressed empathy varies depending on the depth of reasoning. according to @xcite , weak empathy is accompanied by generic expressions such as "are you ok?" or "it's just terrible, isn't it?", while stronger empathy reflects the other's specific situation: "how is your headache, any better?" or "you must be worried about the job interview". in order to respond with stronger empathy, two issues must be tackled: reasoning (i) where to focus on the interlocutor's utterance (for the reason behind the emotion) and (ii) how to generate utterances that focus on such words. firstly, which words should we focus on when empathizing with others? as empathy relates to other's emotional states, the reasons behind emotions (emotion cause) should be identified. imagine you are told "i got a gift from a friend last vacation!" with a joyful face. the likely words that can be the causes of his/her happiness are "gift" and "friend". on the other hand, "vacation" has less to do with the emotion. if you respond "how was your vacation?", the interlocutor may think you are not interested; rather, it is better to say "wow, what was the gift?" or "your friend must really like you." by focusing on the emotion cause words. we humans do not rely on word-level supervision for such affective reasoning. instead, we put ourselves in the other's shoes and simulate what it would be like. perspective-taking is this act of considering an alternative point of view for a given situation. according to cognitive science, perspectivetaking and simulation are key components in empathetic reasoning @xcite @xcite . taking inspiration from these concepts, we propose to train a generative emotion estimator for simulating the other's situation and identifying emotion cause words. secondly, after reasoning which words to focus on, the problem of how to generate focused responses still remains. safe responses that can be adopted to any situations might hurt other's feelings. generated utterances need to convey the impression that concerns the specific situation of the interlocutor. such communicative reasoning is studied in the field of computational pragmatics. the rational speech acts (rsa) framework @xcite formulates communication between speaker and listener as probabilistic reasoning. it has been applied to many tasks to increase the informativeness of generated text grounded on inputs @xcite @xcite . that is, rsa allows the input to be more reflected in the generated output. however, controlling the rsa framework to re-flect specific parts of the input remains understudied. we introduce a novel method for the rsa framework to make models focus on targeted words in the interlocutor's utterance during generation. in summary, we recognize emotion cause words in dialogue utterances with no word-level labels and generate stronger empathetic responses focused on them without additional training. our major contributions are as follows: we aim to improve both pretrained large dialogue agents and empathy-specialized ones by making them focus on emotion cause words in context. emotion cause (pair) extraction. the emotion cause extraction (ece) task predicts causes in text spans, given an emotion. cause spans have been collected from chinese microblogs and news @xcite , english novels @xcite , and english dialogues @xcite . @xcite propose a task of extracting pairs of both emotion and its cause spans. previous works tackle these tasks via supervised learning with question-answering @xcite , jointlearning @xcite , co-attention @xcite , and regularization @xcite . compared to those tasks, we recognize emotion cause words with no word-level labels using a generative estimator. our method does not require word-level labels other than the emotion labels of the whole sentences. we then generate more specific empathetic responses focused on them. rational speech acts (rsa) framework. the rsa framework @xcite has been applied to many nlp tasks including referencing @xcite , captioning @xcite , navigating @xcite , translation (cohn-gordon and goodman, 2019), summarization @xcite , and dialogue @xcite . it can improve informativeness of generated utterances better grounded on inputs (e.g. images, texts). compared to previous use of rsa, we propose an approach that can control the models to focus on targeted words from the given input. ## identifying emotion cause words with generative emotion estimation our approach consists of two steps: (i) recognizing emotion cause words from utterances with no word-level labels ( §3), and (ii) generating empathetic responses focused on those words ( §4). in this section, we first train a generative emotion estimator to identify emotion cause words. ## controlling the rsa framework for focused empathetic responses we introduce how to control the bayesian rational speech acts (rsa) framework (frank and goodman, 2012) to focus on targeted words in the context during response generation. we first preview the basics of rsa for dialogues ( §4.1). we then present how to control the rsa with word-level focus ( §4.2), where our major contribution lies. figure 1 is the overview of our method. ## experiments we first evaluate our generative emotion estimator (gee) on weakly-supervised emotion cause word recognition ( §6.2). we then show our new controlling method for the rsa framework can improve best performing dialogue agents to generate more empathetic responses by better focusing on targeted emotion cause words ( §6.3). ## conclusion we studied how to use a generative estimator for identifying emotion cause words from utterances based solely on emotion labels without word-level labels (i.e. weakly-supervised emotion cause word recognition). to evaluate our approach, we introduce emocause evaluation set where we manually annotated emotion cause words on situations in empatheticdialogues @xcite . we release the evaluation set to the public for future research. we also proposed a novel method for controlling the rational speech acts (rsa) framework @xcite to make models generate empathetic responses focused on targeted words in the dialogue context. since the rsa framework requires no additional training, our approach is orthogonally applicable to any pretrained dialogue agents on the fly. an interesting direction for future work will be reasoning how the interlocutor would react to the model's empathetic response. such reasoning is an essential part for expressing empathy.