TRILOGUE is available for permitted research use under the TRILOGUE Research Use License. Access is granted automatically after users provide the required information and accept the terms below.

By requesting access, you confirm that you have read and agree to comply with the TRILOGUE Research Use License.

TRILOGUE

TRILOGUE is a trilingual spoken-dialogue fact-checking benchmark with clean text, turn-level ASR transcripts, word-level timestamp alignments, evidence supervision, fixed article-disjoint experimental splits, and paired audio. The complete benchmark contains 11,957 dialogues, 187,544 turns, and 390.3 hours of audio in English, Russian, and Kazakh. The current package releases 8,036 Russian and Kazakh recordings (242.5 hours), including 4,998 human-read recordings; 3,921 English TTS recordings are withheld as explained below.

Access to the dataset files is gated. Access is granted automatically after users provide the required information and affirm their agreement to the TRILOGUE Research Use License and the usage restrictions below. By requesting, downloading, or using TRILOGUE, users agree to the License.

English audio availability

English dialogue text, turn-level ASR transcripts, and word alignments are included. The 3,921 English TTS recordings generated with Inworld are withheld from this release because Inworld's current Acceptable Use Policy prohibits using its output in a dataset that may be used to train, fine-tune, develop, test, or improve machine-learning or AI technology. The recordings will not be uploaded unless separate permission is obtained or the applicable provider terms permit their release.

The English rows remain in metadata/audio_manifest.csv for cross-layer provenance. They use audio_release_status=withheld_provider_terms and have a blank audio_path. Russian and Kazakh audio rows use audio_release_status=released.

Dataset structure

data/: one canonical clean-dialogue JSONL per language: 3,921 English, 4,062 Russian, and 3,974 Kazakh records.
audio/: 8,036 released lossless FLAC recordings. Russian and Kazakh have separate human_read/ and tts/ directories. English audio is withheld.
derived/asr/<language>/: one turn-level ASR JSON per dialogue, stored directly under en, ru, or kk.
derived/word_alignments/<language>/: one word/turn alignment JSON per dialogue, stored directly under en, ru, or kk.
splits/: article-disjoint dialogue and article-family assignment manifests for the fixed seeds 13, 42, and 2026. dev is the validation split used in the experiments.
metadata/audio_manifest.csv: the authoritative dialogue-to-audio, ASR, and alignment index, including audio-release status, speech origin, duration, generation version, TTS provenance, and voice-metadata availability.
metadata/dialogue_prompt_map.csv: generation, annotation, and postprocessing identifiers for every dialogue.
metadata/tts_voice_inventory.csv: a deduplicated inventory of 46 synthetic voices, including voices associated with the withheld English audio. It contains no human-participant records.
docs/schema.md: the detailed field, identifier, path, privacy, and split contract.

Dialogue text and available audio are stored once. The three seeded split manifests point to the same canonical records; the release does not duplicate data into physical train, validation, and test directories.

Dialogue identifiers and generation versions

A stable dialogue UID has the form <language>::<version>::<id>, for example english::v1::1.

Version	Paper setting	Generation model	Annotation model
`v1`	Base	Gemini 2.5 Flash	Gemini 2.5 Pro
`v2`	Diversity-controlled	DeepSeek-V3.1 via Azure	Gemini 2.5 Pro
`v3`	Style-randomized	Gemini 2.5 Flash	Gemini 2.5 Pro

Prompt identifiers use v1_artist, v1_judge, and v1_postprocessing, with corresponding v2_* and v3_* identifiers.

The complete generation and annotation prompt templates, runtime placeholders, model settings, directive pools, and cleanup/normalization procedures are reported in the paper appendix section Generation and Annotation Prompts. The official paper URL will be added when the proceedings version becomes available. Prompt text is not duplicated in this repository.

Fixed experimental splits

The splits/ directory contains dialogue-level and article-family-level assignment files for seeds 13, 42, and 2026. Each dialogue manifest contains all 11,957 dialogue UIDs and a split value of train, dev, or test.

All dialogues derived from the same internal article family remain in the same split for a given seed. To reproduce a paper experiment, select the appropriate seed manifest and join it to the canonical data using dialogue_uid. The dev label corresponds to the validation split used in the experiments.

Accessing the gated dataset

Keep the repository authenticated after access is approved. Users may download from the Hugging Face Files tab or with huggingface_hub:

from huggingface_hub import snapshot_download

local_path = snapshot_download(
    repo_id="chaewanC/TRILOGUE",
    repo_type="dataset",
)

See docs/schema.md for field definitions and release paths.

Evidence and source-article linkage

The release includes claim-level evidence_snippet annotations needed for evidence-grounded fact-checking. It does not include the source-article Excel workbook, full source articles, or a duplicate catalogue of article titles, publishers, and URLs.

The split manifests retain an internal article_family_id used to keep related dialogues in the same split. After the collaborators' source-article dataset is published, a mapping from this internal identifier to that dataset's public article identifier will be added without duplicating the source dataset here. Third-party evidence and source materials remain subject to their applicable rights and original access conditions.

Synthetic-audio provenance

Synthetic recordings are identified as TTS in the audio manifest. The released Russian TTS audio was generated using a paid Azure Speech tier and the standard prebuilt neural voices ru-RU-DariyaNeural, ru-RU-DmitryNeural, and ru-RU-SvetlanaNeural. The generation code uses named Azure Speech voices and does not configure a custom-voice endpoint or deployment. Microsoft lists these names among its supported prebuilt Russian voices, and its Product Terms grant paid-tier customers use rights in prebuilt-neural-voice output, subject to the applicable terms.

The English Inworld TTS audio is not released, as described above. Provider and voice metadata are retained so the synthetic status and provenance of all benchmark recordings remain explicit.

Intended uses

TRILOGUE is intended for internal or published research and education on:

Spoken and multilingual fact-checking
Misinformation detection
Evidence retrieval and localization
Automatic speech recognition and ASR-robust reasoning
Multilingual language and speech processing
Evaluation of speech-native, audio-language, and long-context systems

Researchers employed by commercial organizations may use TRILOGUE for a permitted research purpose. This does not authorize incorporation into a deployed commercial product or service.

Users may train, evaluate, and publish research model checkpoints and other learned artifacts when they comply with the License and do not intentionally reproduce, redistribute, or provide practicable access to Dataset records or human-participant recordings.

Controlled generation of false or misleading content is permitted when it is reasonably necessary for a research purpose, clearly identified as generated or false, and handled consistently with the License.

Prohibited and out-of-scope uses

The License prohibits, among other things:

Voice cloning, voice imitation, or impersonation
Speaker identification, speaker verification, re-identification, or biometric profiling
Attempts to discover or infer participant identities or demographics
Redistribution of the Dataset or sharing access credentials
Use in deployed voice-synthesis, biometric, surveillance, advertising, or identity-related products
Generation, amplification, deceptive presentation, or repackaging of false claims for deceptive, harmful, or non-research purposes
Removal or misrepresentation of factuality labels, evidence, provenance, or generated-data status
Reuse outside the stated research purpose
Use of third-party synthetic audio to create, train, or improve a TTS or synthetic-voice product or service where the applicable provider terms prohibit that use

The LICENSE controls if this summary and the full terms differ.

Human recordings and privacy

The project did not collect or release human-participant names, voice IDs, gender, or demographic attributes. Human-read rows in metadata/audio_manifest.csv use voice_metadata_status=not_collected_irb; their voice fields are intentionally blank.

Human speakers read assigned dialogue content rather than providing personal opinions, autobiographical statements, or private information. Adult speakers participated under an institutionally approved protocol and gave informed consent for research use and dataset release. Access is gated because voice recordings may constitute identifiable biometric data.

Human validation was conducted with consenting adult annotators proficient in the relevant language. Only aggregate validation findings are reported; the release does not contain annotator identities or individual-level metadata.

Limitations

TRILOGUE is a controlled benchmark. The dialogues were generated from source articles and controlled hallucinated rewrites rather than collected from naturally occurring misinformation conversations. Results should not be interpreted as measuring performance on every real-world social-media, private-messaging, or adversarial misinformation setting.

Evidence retrieval is source-conditioned within a known associated article family rather than open-domain web source discovery. The current release does not evaluate conflicting sources or temporally changing evidence.

Kazakh speech technology remains a bottleneck, particularly for synthesis, recognition, and alignment quality. The paper baselines use ASR transcripts as the official model input and do not fully evaluate prosody-aware, speaker-aware, or speech-native reasoning over the released audio.

The benchmark includes generated false claims. Examples must not be presented without their generated-data status, factuality labels, and evidence context.

Known data notes

Kazakh TTS dialogue 9574 has audio but no recoverable voice assignment. It is marked missing_voice_assignment.
The source WAVs for Kazakh human-read dialogues 5262, 5294, 5297, 7299, 7322, 7338, and 7340 end before their header-declared durations and before the full scripted dialogues are complete. The released FLAC files preserve all physically available samples. These recordings are included for consistency with the paper-version dataset, but their missing final audio and any alignment timestamps beyond the physical endpoints should be treated as incomplete. We plan to repair these records in a later dataset revision if complete source recordings can be recovered.
Repository paths and Hugging Face language metadata use the ISO codes en, ru, and kk.

Deliberately excluded

English Inworld TTS audio, pending permission or provider terms that permit dataset release
Source-article workbook and full source-article texts
Human-participant identities and individual demographic metadata
Duplicate JSONL/Parquet dialogue copies
Duplicate synthesis-input text folders
Seed-specific copies of train, validation, and test data
Separate public and human-audio repositories
corrections.jsonl, a top-level quality/ directory, and a separate checksums/ directory

Versions

v1.0 — Initial release accompanying the Findings of EMNLP 2026 paper.

Citation

Until the official ACL Anthology record is available, cite:

Chaewan Chun, Meruyert Aristombayeva, Jiyoung Choi, Mahjabin Nahar, Delvin Ce Zhang, and Dongwon Lee.
“TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio.”
Findings of the Association for Computational Linguistics: EMNLP 2026, 2026.

@inproceedings{chun-etal-2026-trilogue,
  title     = {{TRILOGUE}: A Trilingual Spoken Dialogue Fact-Checking
               Benchmark with Evidence and Paired Audio},
  author    = {Chun, Chaewan and Aristombayeva, Meruyert and
               Choi, Jiyoung and Nahar, Mahjabin and
               Zhang, Delvin Ce and Lee, Dongwon},
  booktitle = {Findings of the Association for Computational Linguistics:
               EMNLP 2026},
  year      = {2026}
}

License and contact

TRILOGUE is distributed under the custom TRILOGUE Research Use License.

Contact: Chaewan Chun, czc5884@psu.edu

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