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

Summary

The paper addresses a gap in empathetic dialogue systems: their replies tend to be generic instead of addressing what actually triggered the user's emotion. The authors split this into two subproblems. The first is detecting emotion cause words without token-level annotation. The second is steering generation toward those words. For detection, they train a generative emotion estimator (GEE) on BART. The model learns to produce situation descriptions from EmpatheticDialogues conditioned on an emotion label. Bayes' rule then turns it into an emotion classifier and a per-word scorer of how strongly each word relates to the predicted emotion. For generation, they extend the Rational Speech Acts (RSA) framework so a pragmatic speaker emphasizes chosen input words. This works at inference time and needs no retraining. The authors also release EmoCause, human annotations of cause words for EmpatheticDialogues validation and test situations. Applied to MIME, DodecaTransformer and Blender, the "Focused S1" decoding raises model-based empathy scores and human ratings. This matters because it gives any existing dialogue agent more situation-specific empathy without extra labeled data.

Key points

  • GEE models P(C, E) = P(E)P(C|E) with BART. It reaches perplexity 13.6 on EmpatheticDialogues test situations, which cover 32 roughly balanced emotion labels. A uniform emotion prior is used, and the top-k scored words are taken as emotion cause words.
  • Only sentence-level emotion labels are needed. According to the authors, this weakly supervised cause-word recognition outperforms other baselines.
  • New resource: the EmoCause evaluation set, built from EmpatheticDialogues validation and test situations and publicly released.
  • Focused S1 improves Exploration and Interpretation empathy scores (Sharma et al., 2020) over base S0 for all three agents. In contrast, simply adding emotion labels during finetuning (S0+Emotion) lowered these scores.
  • Human evaluation used Amazon Mechanical Turk with 100 samples, 3 raters each, on 4-point Likert scales (Krippendorff's α of 0.26 for ratings and 0.27 for A/B tests). Raters judged Focused S1 more empathetic, relevant and fluent. In a 200-sample A/B test on Blender, its responses were also judged to reflect cause words better than plain RSA.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

33 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 31 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.emnlp-main.170 · arXiv 2109.08828

@inproceedings{kim2021perspective,
  title = {{Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes}},
  author = {Kim, Hyunwoo and Kim, Byeongchang and Kim, Gunhee},
  booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
  year = {2021},
  doi = {10.18653/v1/2021.emnlp-main.170},
  eprint = {2109.08828},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.emnlp-main.170}
}

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