DuplexGen/duplexgen-corpus

DuplexGen Corpus

Text corpus for DuplexGen: Adaptive Synthesis of Human–AI Turn-Taking Dialogues.

This dataset contains DuplexGen-generated dialogues and our own human turn-taking slot annotations, used to train and calibrate models that predict when a listener should take the floor, backchannel, or stay silent during spoken conversation.

A companion dataset, DuplexGen/duplexgen-spoken, provides a spoken-audio rendering of the generated dialogues (via Chatterbox TTS). The generation pipeline and training/eval code are at github.com/duplexgen/duplexgen-code.

Links

• 📄 Paper — arXiv link coming soon
• 💻 Code — github.com/duplexgen/duplexgen-code
• 🎛️ Finetune — github.com/duplexgen/personaplex-finetune
• 🤗 Corpus — DuplexGen/duplexgen-corpus
• 🤗 Spoken — DuplexGen/duplexgen-spoken
• 🌐 Demo — duplexgen.github.io

Dataset summary

Two parts, laid out per scenario code (TEA, PLN, INT, NEG, PER, SOC):

dialogues/<CODE>/train.jsonl        # DuplexGen-generated dialogues (train-only)
annotations/<CODE>/train.jsonl      # human slot-level turn-taking annotations
annotations/<CODE>/test.jsonl

Only DuplexGen-generated dialogues and our own human slot annotations are released here — no third-party raw source text is redistributed. See Heritage / licensing below for how each scenario's prompts/situations were seeded.

Loading

This repo defines one config per scenario × part: a <CODE>_dialogues config (single train split) and a <CODE>_annotations config (train + test splits) for each scenario code {TEA, PLN, INT, NEG, PER, SOC}:

from datasets import load_dataset

# generated dialogues for one scenario (train-only)
ds = load_dataset("DuplexGen/duplexgen-corpus", "PER_dialogues", split="train")

# human annotations for the same scenario, test split
ann = load_dataset("DuplexGen/duplexgen-corpus", "INT_annotations", split="test")

Valid configs:

• {TEA,PLN,INT,NEG,PER,SOC}_dialogues — split train only
• {TEA,PLN,INT,NEG,PER,SOC}_annotations — splits train, test

Data fields

dialogues/<CODE>/train.jsonl

Each line is one generated dialogue:

{
  "example_id": str,
  "scenario": str,          # scenario code, e.g. "TEA"
  "license": str,           # inherited upstream license, see Heritage / licensing below
  "context": str,
  "style": str,             # e.g. "spoken"
  "disfluency_target": str, # which speaker role disfluency was targeted at
  "speakers": [str, ...],
  "history": [
    {
      "role": str,
      "content": str,
      "segments": [
        # a segment is EITHER a plain content span:
        {"full_content": str},
        # OR a per-word turn-taking decision slot:
        {
          "word_index": int,
          "probs": {
            "floor_taking": float,
            "backchannel": float,
            "silence": float
          },
          "decision": str,          # the sampled/selected action at this slot
          "inserted_token": str | null
        }
      ]
    },
    ...
  ]
}

segments interleaves plain-text spans with per-word turn-taking decision slots in document order — replaying a turn's segments list reconstructs content plus the inserted turn-taking behavior at each slot.

annotations/<CODE>/{train,test}.jsonl

Each line is one dialogue's human slot-level turn-taking annotation. Note the deliberate terminology difference from dialogues: annotations use "silent"/"take_floor" where dialogues use "silence"/"floor_taking" ("backchannel" is shared).

{
  "example_id": str,
  "scenario": str,
  "license": str,
  "history": [
    {
      "role": str,
      "content": str,
      "boundaries": [
        {
          "word_index": int,
          "total_count": int,        # number of human raters for this slot
          "counts": {                # raw vote counts, subset of:
            "silent": int,
            "backchannel": int,
            "take_floor": int
          },
          "probabilities": {         # counts normalized by total_count, over:
            "silent": float,
            "backchannel": float,
            "take_floor": float
          }
        },
        ...
      ]
    },
    ...
  ]
}

Dataset statistics (verified)

dialogues (train-only, generated):

Scenario	Count
TEA	1000
PLN	1000
INT	1000
NEG	1000
PER	999
SOC	1000
Total	5999

Total per-word turn-taking decision segments (slots with word_index/probs, excluding plain full_content segments) across all 5999 dialogues: 125,137.

annotations (human-labeled):

Split	Per scenario	Total (× 6 scenarios)
train	20	120
test	50	300
Total	70	420

Heritage / licensing

Each scenario's dialogues were seeded from a different upstream source and therefore carries a different license. Only the DuplexGen-generated dialogue text and our own human annotations are distributed in this dataset — no raw text from these upstream sources is redistributed here.

Scenario	Code	Source dataset	License
Socratic teaching	TEA	SocraticLM	Apache-2.0
Planning	PLN	MultiWOZ	MIT
Interview	INT	Anthropic Interviewer	CC-BY-4.0
Negotiation	NEG	CraigslistBargain	MIT
Persuasion	PER	DailyPersuasion	Apache-2.0
Social chat	SOC	SODA	CC-BY-4.0

Attribution (PER): the PER scenario is seeded from DailyPersuasion (PersuGPT), released under Apache-2.0. Please retain attribution to DailyPersuasion when redistributing the PER split.

Because licensing differs per scenario, this dataset's top-level Hub license metadata is set to other; the table above is the authoritative per-scenario license reference. If you use only a subset of scenarios, comply with that scenario's license only.

Generator credit

• Dialogue text (spoken-style conversion and synthesized turn-taking dialogues) is generated by Qwen3.5-122B-A10B.
• Spoken audio (in the companion DuplexGen/duplexgen-spoken dataset) is rendered via Chatterbox TTS.
• No third-party raw text is included in this dataset — only DuplexGen-generated dialogues and our own human slot annotations are released.

Citation

If you use this dataset, please cite the DuplexGen paper.