--- license: other license_name: mixed-per-scenario license_link: https://huggingface.co/datasets/DuplexGen/duplexgen-corpus/blob/main/README.md#heritage--licensing pretty_name: DuplexGen Corpus task_categories: - text-generation - conversational language: - en tags: - turn-taking - dialogue - backchannel - full-duplex - spoken-dialogue configs: - config_name: INT_dialogues data_files: - split: train path: dialogues/INT/train.jsonl - config_name: INT_annotations data_files: - split: train path: annotations/INT/train.jsonl - split: test path: annotations/INT/test.jsonl - config_name: NEG_dialogues data_files: - split: train path: dialogues/NEG/train.jsonl - config_name: NEG_annotations data_files: - split: train path: annotations/NEG/train.jsonl - split: test path: annotations/NEG/test.jsonl - config_name: PER_dialogues data_files: - split: train path: dialogues/PER/train.jsonl - config_name: PER_annotations data_files: - split: train path: annotations/PER/train.jsonl - split: test path: annotations/PER/test.jsonl - config_name: PLN_dialogues data_files: - split: train path: dialogues/PLN/train.jsonl - config_name: PLN_annotations data_files: - split: train path: annotations/PLN/train.jsonl - split: test path: annotations/PLN/test.jsonl - config_name: SOC_dialogues data_files: - split: train path: dialogues/SOC/train.jsonl - config_name: SOC_annotations data_files: - split: train path: annotations/SOC/train.jsonl - split: test path: annotations/SOC/test.jsonl - config_name: TEA_dialogues data_files: - split: train path: dialogues/TEA/train.jsonl - config_name: TEA_annotations data_files: - split: train path: annotations/TEA/train.jsonl - split: test path: annotations/TEA/test.jsonl --- # 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`](https://huggingface.co/datasets/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](https://github.com/duplexgen/duplexgen-code). ## Links - πŸ“„ **Paper** β€” arXiv link coming soon - πŸ’» **Code** β€” [github.com/duplexgen/duplexgen-code](https://github.com/duplexgen/duplexgen-code) - πŸŽ›οΈ **Finetune** β€” [github.com/duplexgen/personaplex-finetune](https://github.com/duplexgen/personaplex-finetune) - πŸ€— **Corpus** β€” [DuplexGen/duplexgen-corpus](https://huggingface.co/datasets/DuplexGen/duplexgen-corpus) - πŸ€— **Spoken** β€” [DuplexGen/duplexgen-spoken](https://huggingface.co/datasets/DuplexGen/duplexgen-spoken) - 🌐 **Demo** β€” [duplexgen.github.io](https://duplexgen.github.io/) ## Dataset summary Two parts, laid out per scenario code (`TEA`, `PLN`, `INT`, `NEG`, `PER`, `SOC`): ``` dialogues//train.jsonl # DuplexGen-generated dialogues (train-only) annotations//train.jsonl # human slot-level turn-taking annotations annotations//test.jsonl ``` **Only DuplexGen-generated dialogues and our own human slot annotations are released here β€” no third-party raw source text is redistributed.** See [Heritage / licensing](#heritage--licensing) below for how each scenario's prompts/situations were seeded. ## Loading This repo defines **one config per scenario Γ— part**: a `_dialogues` config (single `train` split) and a `_annotations` config (`train` + `test` splits) for each scenario code `{TEA, PLN, INT, NEG, PER, SOC}`: ```python 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` β€” split `train` only - `{TEA,PLN,INT,NEG,PER,SOC}_annotations` β€” splits `train`, `test` ## Data fields ### `dialogues//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 β€” replaying a turn's `segments` list reconstructs `content` plus the inserted turn-taking behavior at each slot. ### `annotations//{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 (Γ— 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 β€” 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`](https://huggingface.co/datasets/DuplexGen/duplexgen-spoken) dataset) is rendered via **Chatterbox** TTS. - **No third-party raw text** is included in this dataset β€” only DuplexGen-generated dialogues and our own human slot annotations are released. ## Citation If you use this dataset, please cite the DuplexGen paper.