Source-linked AI summary
MIME: MIMicking Emotions for Empathetic Response Generation
Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria
TL;DR
MIME addresses empathetic response generation by modeling positive and negative emotions separately, incorporating emotion mimicry, and sampling emotion mixtures stochastically. Human evaluations and ablation studies show stronger empathetic response generation than existing models, while fluency remains a weakness.
Problem
Empathetic response generation must produce responses appropriate to positive or negative user statements while balancing multiple emotions.
Method
MIME encodes context and emotions, separates positive and negative emotion groups, mimics user emotions, and introduces stochastic emotion-mixture sampling for varied responses.
Results
Human evaluations and ablation studies show MIME is better equipped for empathetic response generation than existing models.
Takeaways & Limitations
Separating positive and negative emotions is important because removing emotion grouping reduces empathy by 0.73 and relevance by 0.02.
Takeaways & Limitations
MIME has worse fluency than other models, and ambiguous emotions such as surprise may require explicit polarity handling.
Abstract
from arXiv · showhide
Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of this polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.
1 Introduction
MIME addresses empathetic response generation by modeling emotion polarity, emotional mimicry, and stochastic variation rather than treating input emotions uniformly. The approach is evaluated against existing methods on a large empathy dialogue dataset.
- 1 Introduction: The paper studies empathetic response generation alongside prior work on empathy understanding, generation, lexicons, and dialogue datasets.These related tasks span newswire, counseling conversations, and dialogue settings.
- 1 Introduction: MIME targets empathetic responses that mimic a speaker’s emotion while accounting for whether the context is positively or negatively charged.It adapts the response’s emotional balance to the input rather than treating all emotions identically.
- 1 Introduction: The model combines context and emotion encoding with emotion stochastic sampling and emotion mimicry to generate appropriate responses for positive and negative statements.Its design builds on emotion mixtures while sampling at the emotion-group level.
- 1 Introduction: MIME exceeds the state of the art when trained and evaluated on a large empathy dialogue dataset.The paper also uses feature ablations to examine the roles of emotion mimicry and emotion grouping.
2 Related Work
Prior work has developed conversational models and emotion-aware generation methods, but generating responses that are both appropriate and empathetic remains challenging. Related approaches include emotion-conditioned generators, emoji-labeled dialogue models, and variational dialogue systems.
- 2 Related Work: Open-domain conversational models can generate persona-consistent and diverse responses, but those responses are not necessarily empathetic.
- 2 Related Work: Emotion-aware generation research has used emotion-category embeddings, emotion-specific generators, and emoji labels in attention-based sequence-to-sequence models.
- 2 Related Work: Variational approaches such as VHRED integrate variational autoencoders with hierarchical sequence-to-sequence dialogue decoders based on Markov assumptions.
3 Methodology
MIME models empathetic response generation around emotion mimicry, separating positive and negative emotion groups and sampling stochastic emotion mixtures. It encodes context and emotion cues, fuses mimicking and non-mimicking representations, and decodes responses from the refined context.
- Emotion Mimicry: MIME assumes empathetic responses mimic user emotion to varying degrees, with negative inputs often combining agreement with positivity.Positive inputs are usually answered positively but may be ambivalent; negative inputs may include hopefulness or a silver lining.
- Context Encoding: The model encodes concatenated context utterances with word, positional, and speaker embeddings, then uses a prepended CTX token as the overall context representation.A transformer encoder produces context-enriched representations, while the CTX representation c summarizes the context.
- Emotion Grouping and Sampling: MIME splits 32 emotions into 13 positive and 19 negative types, sampling a distribution for each group to construct response-emotion representations.The group distributions are normalized with softmax, and each group representation pools its corresponding emotion embeddings.
- Response-Emotion Sampling: Stochastic response-emotion sampling is introduced to produce emotionally more varied responses than deterministic emotion selection.The model samples latent group distributions from the context and converts them into normalized positive and negative emotion mixtures.
- Response-Emotion-Refined Context Fusion: Mimicking and non-mimicking emotion representations are concatenated with context word representations to create response-emotion-refined contexts.A gate determines the contribution of positive and negative refined contexts before subsequent fusion and response decoding.
4 Experimental Settings
MIME is evaluated on EmpatheticDialogues against established empathetic-response baselines using human ratings and pairwise preference judgments. The evaluation emphasizes empathy and relevance because open-domain responses can have multiple valid formulations.
- Dataset: MIME is evaluated on EmpatheticDialogues, which contains 24,850 open-domain dyadic conversations spanning 32 emotion categories.Experiments use the authors’ 8:1:1 train, validation, and test split.
- Baselines: The comparison excludes affective response-generation models requiring an explicitly provided response emotion or lacking a necessary empathy guarantee.MIME is compared with Multi-TR and the state-of-the-art MoEL.
- Human Ratings: Human ratings score model responses from 1 to 5 on empathy and relevance across 128 sampled test instances.Three annotators evaluate four instances for each of the 32 emotion labels.
- Evaluation Metrics: BLEU is retained mainly as a reference metric because a single human-gold response is not necessarily the only correct open-domain response.The evaluation therefore relies primarily on human-assessed metrics following prior work.
- Human A/B Test: A human A/B test asks three annotators to choose between MoEL and MIME for each of the same 128 sampled instances, allowing ties and resolving outcomes by majority voting.The procedure compares the models’ response quality directly on each instance.
5 Results and Discussions
MIME improves empathy and contextual relevance through emotion mimicry, polarity-based grouping, shared emotion embeddings, and stochastic response-emotion mixtures, while sacrificing fluency. Ablations, human evaluations, case studies, and error analysis clarify these benefits and limitations.
- Response-Generation Performance: MIME responses show improved empathy over MoEL and Multi-TR, while human A/B tests more often prefer MIME responses.Annotators also prefer MIME with stochasticity over MIME without it.
- Error Analysis: MIME has worse fluency than other models, with incoherent or semantically unclear utterances observed repeatedly.The authors attribute this trade-off to the decoder emphasizing emotionally apt tokens over stop-words that support grammatical clarity, and suggest additional training samples may help.
- Response-Generation Performance: MIME generates responses that are generally better for both positive and negative user emotions in empathy and fluency.MoEL performs better on negative than positive emotions, possibly reflecting the dataset’s 13 positive and 19 negative emotion labels.
- Ablation Study: Emotion mimicry improves empathy by 0.2, while removing it increases relevance by 0.03 and produces less clearly separated positive and negative emotion clusters.The embedding visualization uses the top two principal components for emotion embeddings with and without mimicry.
- Ablation Study: Removing emotion grouping reduces empathy by 0.73 and relevance by 0.02, supporting separate treatment of positive and negative emotions.Without grouping, emotions may compete within one distribution, producing uniform, dominated, mundane, or generic responses.
- Case Study: MIME often fits context more closely than MoEL, incorporating key information and interpreting positive outcomes rather than fixating on initial negative emotion.Examples include using “interview” and recognizing a frightening story’s positive ending as a “beautiful memory.”
- Error Analysis: Treating surprised as positive can produce inappropriate positivity because surprise may be either positive or negative.The authors propose relabeling negatively surprised instances as shocked.
- Error Analysis: MIME’s emotion-classification accuracies are 34%, 58%, and 77% for top-1, top-2, and top-5, compared with MoEL’s 38%, 63%, and 74%.The authors associate MIME’s stronger top-5 performance with distinguishing positive and negative emotion types.
6 Conclusion
MIME combines emotion grouping, emotion mimicry, and stochastic emotion mixtures for empathetic response generation, but fluency remains a weakness and ambiguous-polarity emotions need further treatment.
- MIME relies on emotion grouping and emotion mimicry to generate empathetic responses.
- Stochasticity in the emotion mixture supports varied response generation.
- Human evaluations and ablation studies indicate that MIME is better equipped for empathetic response generation than existing models.
- MIME falters particularly in fluency, while emotions such as surprise and anticipation may require explicit treatment because of their ambiguous polarity.