Dialogue

SSD (Sub-slot Dialog) dataset: This is the dataset for the ACL 2022 paper "A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots".

The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents > > Xing Han Lu, Siva Reddy, Harm de Vries > > EACL 2023 | | | | | | | :--: | :--: | :--: | :--: | :--: | | Code | Huggingface | Request on Dataverse | Paper | Website | Banner

The Mafia Dataset was created to model the behavior of deceptive actors in the context of the Mafia game, as described in the paper “Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia”. We hope that this dataset will be of use to others studying the effects of deception on language use.

The VlogQA consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube - an extensive source of user-uploaded content, covering the topics of food and travel in the Vietnamese language. This dataset is used for research in Vietnamese Spoken-Based Machine Reading Comprehension.

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.

IndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value. IndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4. Each utterance is labelled along two dimensions: - World Understanding (the degree of world understanding it takes to understand the utterance) - Unambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).