Skip to content

TEIDAN: A Multilingual Multiparty Dialogue Corpus

Sep 2026 · 1 citation · 21 references
Computer Science

TL;DR

This paper describes the collection design, participants, recording setup, transcription format, and corpus statistics, and provides preliminary analyses to illustrate how TEIDAN can support research on turn-taking, addressee recognition, and multimodal grounding in human-human and human-agent interaction.

Abstract

Multi-party interaction is a central setting for human communication and a necessary target for human-agent interaction systems that must participate in group conversation. Yet available corpora often focus on meetings, task-oriented interaction, text-based interaction, or acted scenarios, and fewer resources support cross-linguistic comparison of spontaneous face-to-face triadic discussion. This paper presents TEIDAN, a multilingual multimodal corpus that currently consists of Japanese and English three-party conversations. TEIDAN records groups of three participants discussing open-ended topics with individual pin microphones, a microphone array, and participant-facing cameras, and provides IPU-based transcripts for both language portions. Earlier studies used subsets of the Japanese portion for task-specific benchmarks in multi-party dialogue modeling; in contrast, this paper presents TEIDAN as a corpus resource spanning both Japanese and English, with planned expansion to additional languages. We describe the collection design, participants, recording setup, transcription format, and corpus statistics, and provide preliminary analyses to illustrate how TEIDAN can support research on turn-taking, addressee recognition, and multimodal grounding in human-human and human-agent interaction.

View source

Similar papers

#natural language process... Preprint Sep 2026

BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages

Existing translation models are typically trained on sentence-level and formal text, limiting their ability to capture everyday conversational dialogue phenomena such as informality, speaker interaction, and discourse coherence. Most existing Indic translation resources and evaluation benchmarks focus on sentence-level...

Priyanka Dasari, Yuvrajsinh Bodana, Vandan Mujadia et al. · 0 citations
#artificial intelligence Preprint Sep 2026

SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue

Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and how states change over time. Recent stu...

Hao-Bo Zheng, Tan Tang, Yan Chen et al. · 0 citations
Book Open access Sep 2026

Conversational Style in Open Domain Dialogue Systems: What Makes a Response Sound Natural

Conversational style in this setting is best understood as a pragmatic orientation toward the user rather than toward the system’s own content, and that the system must be mixed-initiative to manifest a conversational style.

Vrindavan Harrison, M. Walker · 0 citations

TalkFa: A Unified Benchmark for Farsi Dialogue Generation and Understanding

Farsi, spoken by more than 120 million people, lacks a comprehensive benchmark for dialogue generation and understanding. We introduce TALKFA, a unified benchmark comprising three complementary datasets: (1) WIKI-FADIAL, 4.2K Wikipedia-grounded dialogues for knowledge-grounded generation; (2) DAILYDIALOG-FA, 6.6K dialo...

Neda Jamshidi, Kamyar Zeinalipour, F. Akbari et al. · 0 citations
#natural language process... Preprint Sep 2026

KABURI-TTS: Phoneme-Keyed Activity-conditioned Bi-channel Utterance Rendering for Interaction

Realizing full-duplex spoken dialogue requires large amounts of two-channel, one-speaker-per-channel conversational speech data. Although conversational text-to-speech (TTS) engines have been developed, they are not necessarily robust to two-party simultaneous phenomena such as backchannels, interruptions, and overlaps...

Ryuichiro Higashinaka, Shinnosuke Takamichi, Tetsuji Ogawa · 0 citations

Related blog posts

Microsoft Research Blog Jul 8, 2026

Flint: A visualization language for the AI era

Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications. The post Flint: A visualization language for the AI era appeared first on Microsoft Research.

MIT News · Artificial Intelligence Sep 30, 2026

This game-playing AI is the new champ at Stratego

Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.