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EmoStance: Response-Side Affective-Orientation Control for Empathetic Response Generation via Emoji Weak Supervision

Ziyuan Jin, Yuxuan Ge, Zheng Tian

arXiv:2609.02133v1cs.AIcs.CL

TL;DR

Empathetic response generation must determine how to affectively and interpersonally take up the previous speaker’s situation, not only what to say. The paper induces a soft response-side orientation from multi-annotator emoji distributions and uses EmoStance to steer a frozen instruction-tuned LLM through continuous prefix embeddings. In blind pairwise evaluation, EmoStance achieves a 62.2% aggregate decisive win rate, with clearest gains in contextual specificity and felt responsiveness.

  • Problem

    Empathetic generation lacks direct supervision for how the next response should be affectively and interpersonally oriented, while listener stance is subjective and underdetermined.

  • Method

    EmoStance uses multi-annotator emoji votes and confidence scores as weak supervision to induce a latent orientation space, predict context- and role-conditioned response orientation, and control a frozen LLM with continuous prefix embeddings.

  • Results

    62.2% aggregate decisive win rate was achieved in blind pairwise evaluation with 20 annotators and 800 judgments, with clearest gains in context specificity and felt responsiveness.

  • Takeaways & Limitations

    The findings support response-side affective orientation as a useful intermediate control signal for contextual uptake and perceived responsiveness.

  • Takeaways & Limitations

    The induced orientation targets come from LLM-provided emoji annotations rather than direct human labels and should be interpreted as corpus-dependent approximations, not an independently validated taxonomy.

Abstract

from arXiv · show

Empathetic response generation requires models to decide not only what to say, but also how to respond to the previous speaker's affective situation. We formulate this as response-side affective-orientation control and use multi-annotator emoji distributions as weak affective--attitudinal evidence, rather than as output symbols or gold labels, to induce a latent control space that operationally approximates listener stance. We construct EmojiDialogue, an utterance-level extension of EmpatheticDialogues with emoji votes and confidence scores, and propose EmoStance, which models source-side affective expression, predicts a soft response-side orientation from dialogue context and speaker roles, and steers a frozen instruction-tuned LLM through continuous prefix embeddings. In blind pairwise evaluation with 20 annotators and 800 judgments, EmoStance achieves a 62.2% decisive win rate, with the clearest gains in contextual specificity and perceived responsiveness, while remaining complementary to external-knowledge methods. Code, annotation metadata, and reconstruction scripts are available in our GitHub repository: https://github.com/18277390221/EmoStance.

1 Introduction

The paper frames empathetic generation as controlling how a response takes up the previous speaker’s affective situation, not merely what it says. It uses ambiguous emoji supervision to induce a soft response-side orientation and realizes that orientation through EmoStance.

  • Problem: Response-side affective orientation captures how the next reply should be affectively and interpersonally positioned before verbalization.It operationally approximates listener stance without assuming direct or gold stance labels.
  • Motivation: Existing emotion, affect, strategy, and dialogue-state variables do not determine whether a reply should provide reassurance, shared excitement, gentle concern, or cautious probing.The paper therefore motivates a soft intermediate variable for response-specific positioning.
  • Motivation: Contexts sharing a coarse emotion label may still require different affective uptake, interpersonal distance, or response strength.Multiple responses can be plausible, making hard single-label supervision unsuitable for fine-grained orientation control.
  • Weak supervision: Multi-annotator emoji votes and confidence scores are aggregated into soft distributions rather than used as output symbols or gold emotion and listener-stance labels.The signals may correspond operationally to orientations such as encouragement, sympathy, celebration, hesitation, teasing, surprise, or concern.
  • Approach: EmoStance predicts context- and role-conditioned response orientation and injects it through learned continuous prefix embeddings into a frozen instruction-tuned LLM.The framework models source-side affective expression and reconstructs a name-free latent orientation space.

2 Related Work

Related work covers affective and supportive dialogue generation, interpersonal stance, emoji supervision, and continuous control of frozen language models. EmoStance differs by using soft emoji-derived latent listener-stance planning rather than predicting emoji or applying manually specified attributes.

  • Empathetic and supportive dialogue generation: Empathetic dialogue research has used explicit emotion conditioning, continuous affect representations, and emotion distributions to ground open-domain responses.Supportive dialogue is commonly evaluated on EmpatheticDialogues.
  • Listener stance and emoji weak supervision: Listener stance concerns how the next speaker takes up the previous turn, rather than the previous speaker’s private emotion.The paper treats this response-side stance as subjective, context-dependent, and often underdetermined.
  • Listener stance and emoji weak supervision: Unlike emoji-supervised response generation, EmoStance neither predicts emoji nor uses a single emoji as a discrete control code.It aggregates multi-annotator votes and confidence scores into soft distributions for latent stance planning.
  • Continuous control for frozen language models: Continuous-control methods steer language models with labels, classifiers, discriminators, instructions, or continuous prompts.EmoStance instead uses a continuous listener-stance vector induced from emoji weak supervision and injected through learned prefix embeddings.

3 Weak Affective-Orientation Supervision

The paper constructs EmojiDialogue as an utterance-level weak-supervision layer over EmpatheticDialogues. Adjacent turns become source–response examples whose emoji votes are aggregated into soft distributions.

  • Dataset construction: EmojiDialogue adds utterance-level weak supervision to EmpatheticDialogues because situation-level emotion labels do not specify response-side affective and interpersonal orientation.The resource collects multiple annotators’ emoji votes for each utterance.
  • Dataset construction: Multi-annotator emoji votes are aggregated into soft emoji distributions.This preserves uncertainty rather than reducing supervision to a single discrete emoji label.
  • Dataset construction: 76,489 source–response examples comprise 58,829/9,263/8,397 train/validation/test instances.Each input contains the situation, dialogue history, and next-speaker marker, while the target is the next utterance.
  • Quality audit: 99.69% of annotations are valid and 99.77% are valid or ambiguous-but-acceptable in a human plausibility audit.The paper also reports construction, audit, licensing, privacy, and release details in appendices.

4 Method: Emoji-Supervised Affective-Orientation Control

EmoStance learns a soft, name-free response-side affective-orientation representation from ambiguous emoji supervision, predicts it from dialogue context and speaker roles, and uses it to control generation. The framework separates affective-interpersonal orientation from its realization as natural language while preserving continuous nuance.

  • 4.4 Prefix-Based Generation Control: EmoStance uses the learned orientation as an intermediate control variable, steering a frozen instruction-tuned language model through prefix embeddings rather than verbalizing the orientation.At inference, the model uses dialogue context and the next-speaker marker without emoji annotations, gold stance labels, response-derived orientation vectors, or the gold response.
  • 4.3 Predicting the Response-Side Affective Orientation: The framework predicts response-side orientation from serialized situation, dialogue history, next-speaker marker, source-side expression, and ordered speaker transition.A neural predictor captures local context, while a role-aware transition prior represents regular affective-uptake patterns such as reassurance after anxious turns.
  • 4.2 Inducing a Name-Free Affective-Orientation Space: Emoji votes and confidence scores are aggregated into soft distributions that preserve disagreement rather than treating emoji as gold emotion or listener-stance labels.The resulting evidence induces latent affective-orientation regions instead of assigning a single hard label.
  • 4.2 Inducing a Name-Free Affective-Orientation Space: EmoStance projects emoji evidence into a name-free latent space whose regions denoise sparse supervision while retaining fine-grained affective information.The model uses relational structure among emoji, including contextual similarity, annotator co-selection, and similar weak affective-attitudinal behavior.
  • 4.3 Predicting the Response-Side Affective Orientation: The predicted orientation distribution is reconstructed through orientation prototypes into a continuous control vector that preserves within-region nuance and reduces sensitivity to weak-label noise.This separates distributional orientation prediction from direct high-dimensional vector regression.
  • 4.3 Predicting the Response-Side Affective Orientation: A gated interpolation combines contextual logits with the role-conditioned transition prior, making the prior a soft structural bias rather than a replacement for contextual prediction.The gate controls how strongly the transition prior contributes to the final orientation distribution.

5 Experiments

Experiments evaluate EmoStance through human preference, automatic diagnostics, component analyses, and efficiency measurements. The strongest supported gains concern aggregate preference, contextual specificity, perceived responsiveness, and deployable component choices, with limitations against some baselines and in runtime cost.

  • Evaluation setup: Blind pairwise human preference is the primary evaluation, complemented by automatic metrics and internal orientation-control diagnostics.Experiments use adjacent-turn component analyses and full EmpatheticDialogues test-set system comparisons under deployable inputs without annotation or reference access.
  • Human evaluation: 62.2% decisive win rate across 800 judgments favored EMOSTANCE overall.The evaluation included 395 wins, 71 ties, 94 neither/both-bad judgments, and 240 losses; the 95% Wilson interval was [58.4, 65.9], with p < .001.
  • Interpretation: Comparisons with LLM-only, LLM-prompt, EmPO-DPO, and Sibyl were statistically inconclusive after the reported tests.The paper therefore does not claim uniform dominance over strong or knowledge-enhanced systems.
  • Human evaluation: 75.9% context-specificity and 73.5% felt-responded decisive win rates concentrated the supported quality gains.Emotion appropriateness and naturalness were more modest, while AI-like/problematic phrasing was statistically inconclusive.
  • Automatic evaluation: Reference-based metrics ranked EMOSTANCE highest among controlled same-backbone systems for BERTScore-F1, ROUGE-L, and BLEU-2.METEOR and diversity diagnostics were mixed, so these results are interpreted narrowly as reference-alignment and surface-form diagnostics.
  • Component analysis: Soft distributional supervision improved response-orientation prediction, while prototype reconstruction raised target-vector cosine similarity from 0.3220 to 0.9236.CE improved from 1.4450 to 1.3792, macro-F1 from 0.3067 to 0.3260, and MSE fell from 0.001058 to 0.000022.
  • Component analysis: The final system achieved 68.1%, 63.9%, and 85.7% decisive win rates over no reranking, no role-aware prediction, and zero control, respectively.These ablations support the orientation signal, role-aware response-orientation prediction, and orientation-consistency selection.
  • Efficiency: B = 4 reranking increased runtime cost 4.02× compared with B = 1 no reranking.The measured throughput was 0.750 versus 3.015 examples/s; candidate generation accounted for 99.48% of B = 4 runtime.

6 Conclusion

The conclusion presents EmoStance as a weakly supervised framework for listener-stance control in empathetic response generation. It reports improved aggregate human preference and contextual uptake, while acknowledging uneven performance, efficiency needs, and unresolved generalization questions.

  • Conclusion: EmoStance uses multi-annotator emoji distributions as soft training signals for response-side stance control.It predicts plausible next-response stances and reconstructs a prototype-structured continuous vector for prefix-based steering of a frozen instruction-tuned language model.
  • Conclusion: 62.2% aggregate decisive win rate across 20 annotators and 800 judgments supports gains in context specificity and felt responded.The conclusion frames listener stance as a useful intermediate variable for contextual uptake and perceived responsiveness.
  • Conclusion: The findings do not indicate uniform superiority across all quality dimensions or strong baselines.Future work targets uncertainty-aware stance prediction, richer and more efficient controls, and evaluation across languages, cultures, datasets, and interaction settings.

Limitations

The paper frames its induced affective-orientation space as corpus-dependent weak supervision rather than a validated listener-stance taxonomy. Its evaluation and capability claims are bounded by short English dyadic conversations, static response-pair studies, and higher-cost reranking.

  • Weak supervision and construct validity: The induced orientation space derives from LLM emoji annotations, not direct human labels, and is not an independently validated taxonomy.Emoji meanings vary across communities, platforms, age groups, and conversational norms; source-corpus and annotator-model biases may remain.
  • Scope and evaluation: Experiments cover short English dyadic EmpatheticDialogues conversations, excluding long-horizon support, multi-party interaction, persistent memory, and open-domain assistants.The reference-conditioned gap also reflects that multiple response orientations may be reasonable for the same context.
  • Scope and evaluation: Human studies evaluate static response pairs rather than live, longitudinal interactions, while some automatic metrics reuse the induced orientation space or related scorers.Those metrics should therefore be treated as internal control-realization diagnostics rather than independent evidence of empathy.
  • Efficiency and capability boundaries: Multi-candidate orientation-consistency reranking improves control at higher inference cost, while single-generation decoding offers a lower-cost alternative.EmoStance targets affective and interpersonal orientation, so it remains complementary to factual, safety, commonsense, and other capability methods.

Ethical Considerations

The paper treats emoji and latent orientations as weak conversational signals rather than ground-truth psychological or clinical labels. It emphasizes deployment safeguards, transparency, privacy, and the method’s complementarity to broader generation capabilities.

  • Interpretation and annotation bias: Emoji annotations and latent orientations are not ground-truth emotions, psychological states, personality traits, clinical indicators, or evidence of internal feelings.Generated responses reflect a selected communicative orientation rather than a diagnosis.
  • Deployment risks: Affective-orientation control could support emotional manipulation, dependency induction, or covert persuasion, so systems should not exploit distress or override safety policies.EmoStance is not designed for diagnosis, crisis counseling, medical or legal advice, or professional emotional care.
  • Privacy, transparency, and release: Benchmark dialogue may contain sensitive personal experiences, requiring consent, compensation, licensing, data protection, privacy checks, and avoidance of identifying information.Users should be informed when interacting with AI and when responses may be guided by inferred affective or interpersonal orientations.
  • Capability boundaries: EmoStance controls affective and interpersonal orientation rather than commonsense reasoning, factual grounding, safety, or surface-form quality.Its contribution is therefore complementary to knowledge-augmented and other optimization methods.
  • Annotation design: The candidate emoji pool intentionally retains boundary cases and rare signals through the union of three screeners’ selections.This preserves potentially meaningful cues accepted by only one screener, while downstream components handle sparsity.
  • Weak-label validation: Human plausibility audits report 99.69% valid annotations and 99.77% valid-or-ambiguous annotations, while exact three-annotator agreement is 95.53%.These audits support contextual plausibility, not gold-standard emotion or listener-stance correctness.

B.7 Human–LLM Distributional Audit

The human–LLM audit compares aggregated emoji distributions before and after projection into a learned nine-region affective-orientation space. Projection reduces divergence and increases overlap, but the audit does not establish human equivalence or cultural universality.

  • Audit design: The audit samples 120 unique test utterances across low-, medium-, and high-disagreement tertiles, with three human annotators per item.The comparison uses the existing four-LLM confidence-weighted distributions and evaluates both emoji-symbol and projected region distributions.
  • Metrics: The audit uses Jensen–Shannon divergence, distributional overlap, and 95% paired bootstrap intervals with 10,000 resamples.The learned emoji-to-region matrix is fixed before the audit.
  • Main findings: −0.237 is the paired change in region-level minus emoji-level JSD, with a 95% interval of [−0.268, −0.206], while mean overlap rises from 0.449 to 0.670.The pattern is consistent with humans and LLMs choosing different symbols associated with similar affective or interpersonal orientations.
  • Interpretation: Exact emoji choices remain variable among human annotators, and projection reduces but does not eliminate disagreement.The audit therefore does not establish equivalence to human annotation, cultural universality, or a complete response-side orientation taxonomy.

C Additional Method Details

The appendix details how the system constructs soft emoji supervision, name-free latent regions, continuous affective vectors, role-aware orientation predictions, and training losses. It also specifies the inference inputs and excludes weak targets and reference responses at deployment.

  • Inference inputs: At inference, EmoStance uses only the situation description, dialogue history, and speaker-role markers.Emoji annotations, latent-region targets, response-derived vectors, and reference responses are unavailable.
  • Soft supervision: Each utterance’s emoji judgments are aggregated into a confidence-weighted soft distribution, preserving disagreement and ambiguity rather than collapsing to a majority label.Emoji outside the screened inventory are discarded and the remaining distribution is renormalized.
  • Latent regions: The name-free emoji graph combines contextual usage and annotator co-selection, then Leiden community detection produces latent affective-orientation regions.Emoji names are not used, and boundary emoji receive soft multi-region membership.
  • Continuous representation: The emoji-to-region matrix maps soft emoji distributions into normalized latent-region distributions, while prototype reconstruction produces continuous control vectors retaining within-region variation.These vectors are later used for response generation and prefix control.
  • Role-aware prediction: The orientation predictor combines contextual encoding, source-side expression, and ordered role transitions to estimate the next response’s affective orientation.An uncertainty-aware gate reduces transition-prior influence when source-expression prediction is highly uncertain.
  • Objectives: Training uses soft cross-entropy for weak orientation targets, with auxiliary source-expression and vector-reconstruction losses.Optional region-frequency weighting addresses latent-region imbalance.

D.3 Human Evaluation Protocol

The human evaluation used blinded pairwise comparisons across five response-quality dimensions, with neutral judgments excluded from decisive win rates. It treats preferences as subjective evidence rather than recovery of a unique correct response.

  • The expanded evaluation used 20 annotators and 800 judgments across two independently sampled batches.Each batch used 10 annotators; the second batch added 400 new judgments.
  • Annotators compared two anonymized responses on emotion appropriateness, felt responded, context specificity, naturalness, and AI-like/problematic phrasing.System names, emoji annotations, latent controls, and response order were hidden.
  • Neutral Tie/Both equally good and Neither/Both bad judgments were retained but excluded from decisive win rates.The AI-like/problematic dimension was reverse-scored so that selecting the more problematic response counted as a loss.
  • The analysis uses individual judgments with Wilson confidence intervals, exact sign tests, and Holm correction across seven baseline comparisons.The evaluation is interpreted as preference evidence because fine-grained dialogue judgments are subjective and multiple orientations may be plausible.

D.5 Focused Human-Ablation Details

The focused ablation evaluates deployable EmoStance variants under the same blind pairwise protocol. Results favor reranking, role-aware orientation prediction, and orientation control, with the strongest comparison against zero control.

  • The focused ablation compared the final system with variants removing reranking, role-aware response-orientation prediction, or orientation control.The expanded study used newly sampled contexts and a second batch of 10 annotators under the same blind pairwise protocol.
  • The ablation study contained 20 annotators and 900 judgments, with 300 judgments for each comparison.Each comparison covered 100 dialogue contexts with three judgments per context.
  • 68.1% was EmoStance’s decisive win rate against the variant without reranking.The judgment counts were 156 wins, 53 ties, 18 neither/both-bad outcomes, and 73 losses.
  • 63.9% was EmoStance’s decisive win rate against the variant without role-aware response-orientation prediction.This comparison yielded 138 wins, 66 ties, 18 neither/both-bad outcomes, and 78 losses.
  • 85.7% was EmoStance’s decisive win rate against zero control.The comparison contained 222 wins, 20 ties, 21 neither/both-bad outcomes, and 37 losses.
  • Across all three comparisons, EmoStance achieved a 73.3% overall decisive win rate, with exact sign-test values below .001.The aggregate included 516 wins, 139 ties, 57 neither/both-bad outcomes, and 188 losses.

D.6 Automatic Main Evaluation

The automatic evaluation treats reference similarity, diversity, generic-response behavior, and orientation-control metrics as diagnostics rather than substitutes for human preference. Results support prototype reconstruction, reranking, and role-aware control while identifying surface-form and prototype-mixture limitations.

  • Automatic metrics compare reference similarity, diversity, generic-response behavior, and orientation-control realization on the full EmpatheticDialogues test set.BERTScore-F1 is the main semantic-similarity measure, while ROUGE-L, BLEU-2, METEOR, Distinct-1/2, Self-BLEU, and Generic provide supplementary diagnostics.
  • Reference-based metrics do not establish superior empathy or naturalness, and diversity diagnostics do not support a uniform diversity claim.EmoStance is neither the most lexically diverse system nor the least generic system.
  • EmoStance can remain preferred on context specificity and felt responded despite a relatively high Generic rate.The Generic diagnostic detects short or formulaic responses but cannot determine whether a response semantically takes up concrete dialogue context; safe, short, or formulaic phrasing remains a surface-level limitation.
  • The full role-aware predictor achieved the best macro-F1, although the context-only predictor had slightly lower CE, JSD, and Brier score.Removing role-aware transition information or the gated transition prior worsened CE, while hard-target supervision substantially degraded CE and macro-F1.
  • Prototype reconstruction outperformed direct regression with a target-vector cosine delta of 0.6015 and an MSE delta of −0.0010.The 95% confidence intervals were [0.5997, 0.6033] for cosine similarity and [−0.00105, −0.00103] for MSE, favoring prototype reconstruction.
  • The deployable control vector is constrained to mixtures of nine prototypes and may omit response-specific residual variation.Prototype reconstruction demonstrates predictability under the proposed supervision but does not establish lossless reconstruction.
  • Reranking produced the strongest deployable orientation-consistency diagnostics and reduced generic-response degeneration relative to non-reranked role-aware control.The reranked deployable system also obtained the best supplementary accuracy, Distinct-2, and Self-BLEU values among deployable variants.

E.3.1 Inference-Efficiency Benchmark

The efficiency benchmark compares single-generation decoding with four-candidate orientation-consistency reranking under identical hardware and decoding settings. Reranking improves preference and control diagnostics but incurs roughly fourfold inference cost, with results specific to the tested setup.

  • The benchmark compared B = 1 single-generation decoding with B = 4 four-candidate orientation-consistency reranking.Both configurations used the same model checkpoints and generation settings on one NVIDIA RTX 4090.
  • B = 4 increased mean latency from approximately 0.332 seconds to 1.333 seconds per example, a 4.02× relative cost increase.Throughput decreased from 3.015 to 0.750 examples per second, and the added cost arose almost entirely from generating extra candidates.
  • Four-candidate generation accounted for 99.48% of quality-oriented runtime, while orientation scoring and final selection together accounted for 0.52%.The computational overhead therefore scales primarily with the number of generated candidates.
  • The reranked system achieved a 68.1% decisive win rate over no reranking in the expanded human ablation.The paper presents B = 1 as the lower-cost deployment mode and B = 4 as the quality-oriented mode trading approximately fourfold inference cost for higher human preference and stronger orientation consistency.
  • The reported latency and throughput are controlled within-system comparisons rather than universal deployment figures.They depend on hardware, implementation, prompt and response lengths, batch configuration, and decoding settings.
  • The study involved volunteer annotators, did not seek formal ethics review, and did not conduct a separate exhaustive PII audit.The authors cannot guarantee that the original benchmark contains no residual identifying information.
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