For goal-oriented document-grounded dialogs, it often involves complex contexts for identifying the most relevant information, which requires better understanding of the inter-relations between conversations and documents. Meanwhile, many online user-oriented documents use both semi-structured and unstructured contents for guiding users to access information of different contexts. Thus, we create a new goal-oriented document-grounded dialogue dataset that captures more diverse scenarios derived from various document contents from multiple domains such ssa.gov and studentaid.gov. For data collection, we propose a novel pipeline approach for dialogue data construction, which has been adapted and evaluated for several domains.
HINT3 is a dataset for intent detection. It consists of 3 different datasets each containing a diverse set of intents in a single domain - mattress products retail, fitness supplements retail and online gaming named SOFMattress, Curekart and Powerplay11. Source: https://github.com/hellohaptik/HINT3
In MutualFriends, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend. Source: Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
ClovaCall is a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people. The raw dataset of ClovaCall includes approximately 112,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain. Source: https://github.com/ClovaAI/ClovaCall
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The Permuted bAbi dialog task is an adaptation of the "Dialog bAbI tasks data" dataset released by Facebook. It is used for evaluating end-to-end dialog systems in the restaurant domain. This dataset introduces multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of end-to-end goal-oriented dialog systems in a more realistic setting. Source: https://github.com/IBM/permuted-bAbI-dialog-tasks
This dataset contains synthetically generated discussions and annotations using exclusively Large Language Model (LLM) agents. Discussions are performed between randomly selected users, with a LLM moderator/facilitator following various facilitation strategies. Each LLM user and annotator use a distinct Sociodemographic Background. User-agents also have different roles when joining the discussion. Each discussion consists of 28 comments - 14 participant comments and 14 moderator interventions. Each comment is annotated by 10 different LLM annotators for toxicity and argument quality. Designed to analyze facilitation strategies and synthetic online discussion simulation but can also be used for finetuning LLM facilitators.