Datasets:

nivvis
/

eq-esconv-sifted

license: cc-by-nc-4.0
parent_dataset: thu-coai/esconv
task_categories:
  - text-generation
  - conversational
tags:
  - empathy
  - emotional-intelligence
  - eq
  - emotional-support
  - ranked
language:
  - en
size_categories:
  - 1K<n<10K

EQ-ESConv-Sifted: Elo-Ranked Emotional Support Conversations

The ESConv dataset (Liu et al., ACL 2021) ranked by empathetic quality via Swiss-style Elo tournament. All 1,300 conversations scored and sorted.

Why this exists

ESConv is a widely-used emotional support dataset but quality varies significantly — some conversations have excellent empathetic support, others are low-effort or off-topic. This dataset adds Elo rankings so you can filter by quality.

For higher-quality synthetic empathetic conversations, see:

nivvis/eq-convos — 1,774 multi-turn synthetic conversations, Elo-ranked
nivvis/eq-dpo — 2,880 turn-level DPO preference pairs

This dataset is still useful as:

Source material for further synthesis
A benchmark for comparing empathy quality across datasets
SFT data when filtered to top tiers

Fields

Field	Description
`id`	Original ESConv index
`elo`	Tournament Elo rating (higher = better empathy)
`wins`	Tournament wins
`losses`	Tournament losses
`draws`	Tournament draws
`emotion_type`	Primary emotion: anxiety, depression, sadness, anger, fear, etc.
`problem_type`	Problem category: breakup, job crisis, ongoing depression, etc.
`situation`	Description of the seeker's situation
`dialog`	Full conversation as JSON array of `{text, speaker, strategy}` turns
`survey_score`	Original crowdworker survey ratings (empathy, relevance, emotion intensity)

Quality Tiers

Tier	Elo	Count
Top 20%	>= 1530	~260
Top 50%	>= 1500	~650
Bottom 25%	< 1480	~325

Tournament Method

Swiss-style pairing: similar-Elo conversations matched each round
10 rounds, ~650 matches per round
Logit-probe A/B judging: normalized P(A|{A,B}) from single-token output
Position bias mitigated via randomized A/B presentation order
Continuous Elo scoring (no binary win/loss, raw probabilities as match scores)

Citation

Original ESConv dataset:

@inproceedings{liu-etal-2021-towards,
    title = "Towards Emotional Support Dialog Systems",
    author = "Liu, Siyang and others",
    booktitle = "ACL 2021",
    year = "2021"
}