---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- conversation
- proactive
- multi-party-dialogue
- addressee-detection
- turn-taking
size_categories:
- 10K<n<100K
---

# Dataset Card for Multi-Party Dialogue

## Dataset Details

### Dataset Description

This dataset is designed for **proactive decision prediction (SPEAK or REMAIN SILENT)** in multi-party conversations: given a conversational context and a current utterance, the task is to predict whether a specific participant (the *target speaker*) will speak next (`SPEAK`) or remain silent (`SILENT`).

Each sample captures a decision point in a conversation — a moment where the target speaker either takes the floor or stays silent. The dataset is balanced (50/50 SPEAK/SILENT) and spans three domains: **meeting transcripts (AMI, SPGI)** and **TV show dialogues (Friends)**.

- **Curated by:** Ishiki Labs
- **Language(s):** English
- **License:** Apache 2.0

### Dataset Sources

- **AMI Corpus:** Meeting recordings and transcripts: [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/)
- **Friends Corpus:** TV show transcripts from the sitcom *Friends* [Friends Corpus](https://github.com/yellow-binary-tree/Friends-MMC)
- **SPGI2.0 Corpus:** Earnings call transcripts from S&P Global [SPGI2.0](https://datasets.kensho.com/datasets/spgispeech2)

## Dataset Structure

### Splits and Sizes

| Dataset | Train  | Val   | Test  | Total  |
|---------|--------|-------|-------|--------|
| AMI     | 11,270 | 1,408 | 1,410 | 14,088 |
| Friends | 10,289 | 1,286 | 1,287 | 12,862 |
| SPGI    | 79,432 | 9,929 | 9,929 | 99,290 |

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `decision_point_id` | string | Unique identifier for the decision point |
| `sequence_id` | string | ID of the conversation sequence |
| `meeting_id` | string | Source meeting or episode ID |
| `target_speaker` | string | The speaker whose next-turn behavior is predicted |
| `all_speakers` | list[str] | All active participants in the conversation |
| `turn_index` | int | Index of the current turn in the sequence |
| `turn_type` | string | Whether the current turn is `addressing` or `context` |
| `source` | string | Annotation source (e.g. `explicit`, `friends_explicit`, `spgi_heuristic`) |
| `is_explicit` | bool | Whether the addressee is explicitly marked |
| `inference_confidence` | int | Confidence score (1–10) for the addressee inference |
| `context_turns` | list[dict] | Prior turns with `speaker` and `text` fields |
| `current_turn` | dict | The current utterance with `speaker` and `text` |
| `addressees_in_current` | list[str] | Speakers explicitly addressed in the current turn |
| `target_is_addressed` | bool | Whether the target speaker is directly addressed |
| `target_spoke_next` | bool | Ground truth: did the target speaker take the next turn? |
| `decision` | string | Label: `SPEAK` or `SILENT` |
| `confidence` | string | Confidence of the label (`high`, `medium`, `low`) |
| `reason` | string | Human-readable explanation for the label |
| `category` | string | Fine-grained label category (see below) |
| `num_context_turns` | int | Number of context turns provided |
| `num_query_words` | int | Word count of the current turn |
| `current_turn_text_length` | int | Character length of the current turn |
| `is_filler` | bool | Whether the current turn is a filler utterance |

### Label Categories

| Category | Description |
|----------|-------------|
| `SPEAK_explicit` (I1) | Target is directly addressed and speaks next |
| `SPEAK_implicit` (I2) | Target speaks next without being explicitly addressed |
| `SILENT_no_ref` (S1) | Target is not addressed and does not speak next |
| `SILENT_ref` (S2)| Target is referenced/addressed but does not speak next |


### Files per Split

Each split contains:
- `{split}_samples.jsonl` — the main samples
- `{split}_samples_with_reasoning.jsonl` — same samples with an added `reasoning` field explaining the label
- `filtering_summary.json` — summary statistics from dataset curation
- `stage4_filtered_samples.jsonl` — all filtered samples before train/val/test splitting

## Dataset Creation

### Curation Rationale

Multi-party conversations require participants (and models) to track who is being addressed and anticipate who will speak next. Existing dialogue datasets largely focus on two-party settings. This dataset was created to support research on **proactive, context-aware response prediction** in realistic multi-party settings with 2–7 participants.

### Source Data

#### Data Collection and Processing

1. **AMI Corpus** — Meeting transcripts with explicit addressee annotations. Decision points were extracted at each turn where a speaker change could occur.
2. **Friends Corpus** — TV show scripts with scene-level speaker turns. Explicit addressee mentions (e.g., "Hey Joey, ...") were used to label addressee information.
3. **SPGI Corpus** — Earnings call transcripts. Addressees were inferred heuristically from conversation structure (e.g., question-answer pairs, moderator patterns).

Each decision point consists of a fixed-length context window, a current utterance, and a binary label indicating whether the target speaker took the next turn. The dataset was filtered to remove low-confidence samples and balanced to 50/50 SPEAK/SILENT via stratified sampling.

#### Who are the source data producers?

- **AMI Corpus:** Recorded and annotated by the AMI Consortium (University of Edinburgh and collaborators)
- **Friends Corpus:** Transcripts from the TV show *Friends* (NBC)
- **SPGI Corpus:** Earnings call transcripts from S&P Global Market Intelligence

#### Annotation Process

- **AMI / Friends:** Labels are derived from ground-truth speaker-turn sequences and explicit addressee annotations in the original corpora.
- **SPGI:** Labels are derived heuristically from turn-taking patterns; `inference_confidence` reflects annotation certainty.
- A `reasoning` field (in `*_with_reasoning.jsonl` files) provides a natural language explanation for each label, generated to support chain-of-thought training.

#### Personal and Sensitive Information

- AMI and SPGI data may contain real names of meeting/call participants. No additional anonymization has been applied beyond what is present in the original corpora.
- Friends data is fictional dialogue from a TV show.


## Citation

If you use this corpus, please cite our work:
```bibtex
@misc{bhagtani2026speakstaysilentcontextaware,
  title={Speak or Stay Silent: Context-Aware Turn-Taking in Multi-Party Dialogue},
  author={Bhagtani, Kratika and Anand, Mrinal and Xu, Yu Chen and Yadav, Amit Kumar Singh},
  year={2026},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2603.11409}
}
```