---
language:
- en
license: mit
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
tags:
- text
- multi-party-dialogue
- deliberation
- collaborative-reasoning
- chess
- dialogue-acts
- epistemic-stance
- human-annotations
- gemini-annotations
size_categories:
- 1K<n<10K
pretty_name: DeliChess
configs:
- config_name: utterances
  default: true
  data_files:
  - split: train
    path: data/utterances.csv
- config_name: events
  data_files:
  - split: train
    path: data/events.csv
- config_name: dialogues
  data_files:
  - split: train
    path: data/dialogues.csv
---

# DeliChess

DeliChess is an English multi-party dialogue dataset for studying group
deliberation in chess puzzle solving. Participants first selected moves
independently, deliberated synchronously in groups, and then submitted revised
answers. The corpus contains 107 dialogues, 7,667 chat utterances, and 321
dialogue--puzzle cases. Its event stream also records the puzzle options and
paired pre-/post-deliberation choices.

Every genuine chat utterance has human annotations for communicative function,
epistemic stance, and deliberative-agent usefulness. This release additionally
includes independent Gemini 3.5 Flash predictions for the same three dimensions
on all 7,667 utterances. The Gemini predictions are an auxiliary comparison
layer, not replacements for the human annotations.

Paper: [DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess
Puzzle Solving](https://arxiv.org/abs/2606.04987)

## Dataset configurations

- `utterances` (default): one row per genuine chat utterance, with clean text,
  dialogue-local speaker identifiers, human labels, Gemini predictions, and
  agreement flags.
- `events`: the complete 12,596-row ordered task record. It includes chat,
  puzzle specifications, independent and revised submissions, and task-state
  events. Annotation fields are blank for non-chat events.
- `dialogues`: one privacy-safe summary row per dialogue.

All data are provided as UTF-8 CSV. Hugging Face calls the sole split `train`
for loading purposes, but the release does not prescribe a train/test split.
Researchers should split by `dialogue_id`, not by utterance.

See [USAGE.md](USAGE.md) for loading examples and
[docs/ANNOTATION_GUIDELINES.md](docs/ANNOTATION_GUIDELINES.md) for the complete
annotation definitions.

## Annotation schema

### Communicative function

`PROPOSE_ANSWER`, `PROVIDE_REASONING`, `EXPLORE_ALTERNATIVES`,
`REQUEST_REASONING`, `EVALUATE_CRITIQUE`, `AGREEMENT_ALIGNMENT`,
`COORDINATE_DECISION`, `SOCIAL_MODERATION`, and `SOCIAL_OFFTASK`.

### Epistemic stance

`NO_TASK_STANCE`, `HEDGED`, and `NO_HEDGED`. The last is the historical name
for an unhedged task stance.

### Deliberative-agent usefulness

`HELPFUL`, `NEUTRAL`, `NOT_APPLICABLE`, and `HARMFUL`. This dimension asks
whether a conversational move would be useful for an agent supporting group
deliberation; it is not a generic sentiment or quality label.

## Data fields

The `utterances` configuration contains:

- anonymous dialogue, utterance, and event IDs;
- within-dialogue utterance and event positions;
- dialogue-local speaker ID and study-assigned animal alias;
- cleaned utterance text and time elapsed from the dialogue's first event;
- three `human_*` annotation columns;
- three `gemini_*` prediction columns;
- per-dimension and all-three agreement flags; and
- an optional, privacy-screened annotator note.

The `events` configuration additionally contains the event type, original
privacy-screened event payload, task-state fields, and blank annotation fields
for non-chat rows. `WASON_INITIAL` stores the three puzzle specifications and
candidate moves; `GAME_SUBMIT` and `WASON_SUBMIT` record independent and revised
selections, respectively.

## Dataset creation

The task pool contains 24 chess puzzles: eight endgame, eight positional, and
eight tactical. Each dialogue uses one puzzle from each category and offers
five candidate moves, including the Stockfish-best move and four plausible
distractors. Participants first answer independently, then deliberate, and
finally revise their choices.

The corpus annotations were collected from trained proficient English speakers
recruited through Prolific. The complete guidelines are included in this
repository. The study protocol received institutional ethics approval and
participants gave informed consent.

For the auxiliary model layer, Gemini received the complete dialogue context
and the same canonical guidelines but never the human labels. Inference used
Gemini 3.5 Flash at temperature 0. Detailed configuration and agreement results
are documented in [docs/GEMINI_ANNOTATION.md](docs/GEMINI_ANNOTATION.md).

## Anonymisation and sensitive content

The release contains no Prolific IDs, assignment/session IDs, source message or
user IDs, absolute timestamps, private paths, contact details, or reverse
mapping. Speakers are reindexed independently within each dialogue. The animal
names in `speaker_name` are aliases assigned by the study, not real names.
Incidental personal names and demographic disclosures found during screening
were masked.

The dialogues are task-focused. Manual screening found no hate speech, threats,
or targeted abuse, although a small number of mild expletives remain as part of
the natural dialogue record.

## Intended uses and limitations

DeliChess supports research on multi-party dialogue, collaborative reasoning,
dialogue acts, epistemic stance, deliberation outcomes, and deliberation-support
agents. It should not be used to identify participants or infer sensitive
attributes.

The data come from a controlled, text-only chess task and may not generalize to
face-to-face deliberation, other domains, or other participant populations.
Most main-corpus utterances have one human annotation, so the release does not
provide independent duplicate labels for every dialogue. Utterances are nested
within dialogues and should not be treated as independent observations.

Gemini agreement with the human annotations is uneven across dimensions and
labels. These predictions are model outputs rather than adjudicated annotation,
and downstream users should keep their provenance explicit.

## Licensing

The dataset is released under the MIT License. See [LICENSE](LICENSE).

## Citation

```bibtex
@misc{zhu2026delichessmultipartydialoguedataset,
      title={DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving}, 
      author={Xiaochen Zhu and Georgi Karadzhov and Tom Stafford and Andreas Vlachos},
      year={2026},
      eprint={2606.04987},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.04987}, 
}
```