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Towards Persona-Based Empathetic Conversational Models

Paper · 2020 · Peixiang Zhong, Chen Zhang, Hao Wang et al.

Summary

Prior work on empathetic dialogue agents mostly ignored who is speaking. Psychology links persona to personality, and personality in turn shapes empathy. The authors also found that, in EMPATHETICDIALOGUES, responses from different speakers diverge more than random response sets. To study this link, they define a task of persona-based empathetic conversation and build PEC. PEC is a large multi-turn Reddit corpus covering two emotionally contrasting communities, and each speaker comes with persona sentences mined from their own posting history.

They also introduce CoBERT, a BERT-based response selection model. It uses multi-hop co-attention to match context, persona and candidate responses. CoBERT beats strong baselines on PEC by wide margins, including Poly-encoder and several BERT-adapted models.

Their controlled experiments mix empathetic and casual training data. Persona information helps empathetic responding noticeably more when the model learns from empathetic conversations. The authors present this as the first empirical evidence connecting persona and empathy in dialogue. The finding matters for designing configurable, supportive conversational agents.

Key points

  • PEC data and annotation:
    • Sources are r/happy (157K training conversations) and r/offmychest (124K training conversations).
    • Data is split 8:1:1 by thread, and each conversation keeps up to 6 turns.
    • Each user gets up to 100 filtered persona sentences, which start with "i" and contain 4–20 words.
    • Human-rated empathy is 0.73 for happy and 0.61 for offmychest, compared with 0.25 for a casual control group.
  • Motivating analysis: in 28 of 32 emotions, the TF-IDF similarity between two speakers' responses was lower than the similarity between random disjoint response sets.
  • Persona gain by training data: on empathetic test data, adding persona improved R@1 by 6.32% when CoBERT was trained on PEC, versus 3.77% when trained on casual data (p < 0.001). The gain on casual responding stayed at roughly 3.3–3.4%.
  • Number of persona sentences:
    • Validation R@1 rises from 40.4% with no persona sentences to 46.2% with 10 and 47.1% with 20.
    • Memory use grows quadratically with the number of sentences, and 30 sentences caused a memory error.
  • Unseen speakers: 42.1% of test examples come from new speakers. Persona still helps on these examples (38.5 → 43.2 R@1), with an overall test gain of 39.6 → 45.1.

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

 Institutions

🏢Alibaba GroupHangzhou · China64 works · 14,113 citations🎓Nanyang Technological UniversitySingapore · Singapore73 works · 24,390 citations🔬Joint NTU-UBC Research Centre of Excellence in Active Living for the ElderlySingapore · Singapore2 works · 534 citations

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksHuman-Computer Interaction · 61 works

🧭 Topics 1Research subjects and languages the work is about.Dialogue systems · 401 works

Cite

Cite as Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu and Chunyan Miao. 2020. Towards Persona-Based Empathetic Conversational Models. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.emnlp-main.531

@inproceedings{zhong2020persona,
  title = {{Towards Persona-Based Empathetic Conversational Models}},
  author = {Peixiang Zhong, Chen Zhang, Hao Wang, Yong Liu and Chunyan Miao},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.531},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.531}
}

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