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Goal-Oriented Dialogue Systems datasets

archive 2025-07-28

5 datasets carry the task tag "Goal-Oriented Dialogue Systems" (the task itself: Goal-Oriented Dialogue Systems), ordered by the archive's paper count. Page 1 of 1: 5 shown of 5. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Goal-Oriented Dialogue Systems datasets 1–5 of 5

MultiDoc2Dial (MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents)
MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.
26 papers · 0 benchmarks
MMD (Multimodal Dialogs)
The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations.
18 papers · 0 benchmarks
DSTC7 Task 1 (Dialog System Technology Challenges Task 1)
The DSTC7 Task 1 dataset is a dataset and task for goal-oriented dialogue.
12 papers · 1 benchmark
MetaLWOz (Meta-Learning Wizard-of-Oz)
Collected by leveraging background knowledge from a larger, more highly represented dialogue source.
4 papers · 0 benchmarks
To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment.
1 paper · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.