OpenDialog Dataset: Multi-modal Dialogue Research

Updated 17 July 2025

OpenDialog is a collection of diverse datasets featuring text-based, retrieval, and spoken dialogue modalities for open-domain research.
It supports interactive learning and multi-turn conversational modeling through rigorously designed extraction and annotation pipelines.
Benchmarks use metrics like Efficiency Score, MAP, and WER, guiding improvements in dialogue system evaluation and synthesis.

OpenDialog is a designation applied to multiple datasets supporting research on open-domain dialogue, interactive learning, multi-modal interaction, and spoken dialogue synthesis. Despite their independent provenance across several major papers, these datasets share the name "OpenDialog" and have collectively contributed to advances in dialog system evaluation, interactive knowledge acquisition, multi-turn conversational modeling, and large-scale spoken dialogue generation.

1. Dataset Variants and Core Properties

Several datasets titled "OpenDialog" have emerged, each targeting a distinct research axis:

Variant	Scale	Modality	Source/Data Type	Distinctive Feature
Interactive QA (2016, (Vodolán et al., 2016))	1,900 dialogs, 8,533 turns	Text/dialog acts	CrowdFlower/Expert annotated	Interactive learning, denotation
Reddit–Wiki retrieval (Harel et al., 2022)	846 test dialogues	Text, Wikipedia	Reddit threads, MTurk annotation	Sentence retrieval for open-ended dialog
Spoken (ZipVoice, (Zhu et al., 12 Jul 2025))	6.8k hours, 2 languages	Speech/transcripts	In-the-wild recordings, LLM filter	Two-party spoken, stereo, multi-lingual

The scale of these collections ranges from thousands of short, annotated text dialogues to nearly 7,000 hours of transcribed speech, reflecting the varied needs of dialogue system research.

2. Data Collection and Curation Methodologies

Each OpenDialog dataset utilizes a rigorously designed extraction and annotation pipeline:

Interactive QA (2016, (Vodolán et al., 2016)): Dialogs were elicited via a three-phase protocol on CrowdFlower, involving question paraphrasing, explanation, and answering, with subsequent expert verification of factual reference (to Freebase entities). Dialog acts were labeled with rule-based NLU taggers.
Reddit–Wiki retrieval (Harel et al., 2022): Threads were sampled from Pushift.io archives by enforcing initiator alternation and minimum turn count; Wikipedia candidate sentences were retrieved via LLMing and IR heuristics; human relevance judgements obtained for the test set via Mechanical Turk masters.
Spoken OpenDialog (ZipVoice, (Zhu et al., 12 Jul 2025)): Large-scale in-the-wild recordings were processed through voice activity detection, speaker diarization, WhisperD-based ASR with speaker attribution, and LLM-based filtering for genuine two-party dialogue extraction. Quality control included rule-based filters and DNSMOS-based voice quality screening.

These methodologies yield datasets with controlled properties: explicit turn structure, reliable speaker labels, and annotation for dialog state or factual grounding.

3. Technical Frameworks and Evaluation Protocols

OpenDialog datasets are accompanied by transparent evaluation protocols and are referenced in benchmarks:

Interactive QA (Vodolán et al., 2016):
- Models are assessed via Efficiency Score ( $S_D$ ), penalizing unnecessary explanation/answer requests and incorrect answers:
$S_D = \frac{n_c - w_i n_i - w_e n_e - w_a n_a}{|D|}$

with recommended $w_i=5$ , $w_e=0.2$ , $w_a=1$ . - Answer Extraction Accuracy quantifies correct entity extraction from user-provided hints.
Sentence Retrieval (Harel et al., 2022):
- Uses IR metrics: Mean Average Precision (MAP), NDCG@5, and MRR.
- Neural retrieval methods (BERT-based rerankers) are trained with weak supervision using pseudo relevance labels constructed by reciprocal rank fusion over multiple IR and neural similarity scores.
Spoken Dialog Synthesis (Zhu et al., 12 Jul 2025):
- Benchmarks include Word Error Rate (WER), concatenated permutation WER (cpWER) for speaker turn assignment, speaker similarity (cpSIM), UTMOS for speech quality, inference Real-Time Factor (RTF), and subjectively, CMOS and SMOS.

Baselines are provided in each setting, and model improvements are measured both by absolute gains and statistical significance.

4. Supported Research Paradigms and Applications

The various OpenDialog datasets underpin research in:

Interactive and Incremental Learning: The original (Vodolán et al., 2016) dataset enables dialog agents to learn new knowledge from user input through interaction, supporting research on active learning, paraphrase acquisition, and context clarification.
Sentence Retrieval in Dialogue Contexts: As in (Harel et al., 2022), models leverage OpenDialog to identify sentences from external corpora (e.g., Wikipedia) relevant for generating or continuing dialogue turns—extending the evaluation of conversational search and open-domain dialog response retrieval.
Spoken Dialogue Generation: (Zhu et al., 12 Jul 2025) introduces a large-scale benchmark for non-autoregressive, speaker-aware audio synthesis, targeting conversational TTS, zero-shot dialogue generation, and stereo spoken synthesis.
Knowledge-Base Integration and Open-Domain QA: These datasets facilitate the connection between free-form conversation, entity grounding, and structured KB query, supporting improved factuality in dialog systems.

Potential applications include:

Virtual and personal assistants
Customer support bots
Immersive conversational agents with stereo or multi-lingual speech
Systems for continual knowledge augmentation via dialog

5. Data Access and Usage

Repositories and Licensing:
- For spoken OpenDialog (Zhu et al., 12 Jul 2025), data, code, checkpoints, and demo samples are public via https://github.com/k2-fsa/ZipVoice; users should consult repository licensing for details.
- Earlier variants, such as the 2016 Interactive QA Corpus (Vodolán et al., 2016), are typically released upon request or subject to license.
Dataset Organization:
- Splits (e.g., train/dev/test) are provided with breakdowns for supervised experimentation.
- Annotations may encompass dialog acts, speaker roles, factual grounding, and per-turn audio/text alignment.

These access provisions favor reproducible research and cross-comparison among competing dialog models.

6. Impact and Trajectory in Dialogue System Research

Across its incarnations, the OpenDialog name signifies resources that address key dialog system bottlenecks:

Data sparsity and generalization: By eliciting if-necessary clarifications and paraphrases, the datasets help mitigate fact coverage gaps in open-domain settings (Vodolán et al., 2016).
Evaluation realism: In-the-wild speech and naturally noisy, user-generated dialog ensure that systems trained on OpenDialog variants are robust to real-world deployment conditions (Zhu et al., 12 Jul 2025).
Weakly supervised scale-up: (Harel et al., 2022) demonstrates substantial gains in retrieval efficacy by constructing pseudo-labels tailored to dialog context, exceeding transfer learning on generic QA datasets like MS MARCO.

A plausible implication is that OpenDialog datasets continue to shape benchmarks and baselines in both text-based and spoken dialog system research, specifically as large-scale, realistic, and flexibly annotated corpora become central to next-generation conversational AI development.