Intent Discovery

The ATIS (Airline Travel Information Systems) is a dataset consisting of audio recordings and corresponding manual transcripts about humans asking for flight information on automated airline travel inquiry systems. The data consists of 17 unique intent categories. The original split contains 4478, 500 and 893 intent-labeled reference utterances in train, development and test set respectively. Source: Spoken Language Intent Detection using Confusion2Vec

The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity: * SearchCreativeWork (e.g. Find me the I, Robot television show), * GetWeather (e.g. Is it windy in Boston, MA right now?), * BookRestaurant (e.g. I want to book a highly rated restaurant in Paris tomorrow night), * PlayMusic (e.g. Play the last track from Beyoncé off Spotify), * AddToPlaylist (e.g. Add Diamonds to my roadtrip playlist), * RateBook (e.g. Give 6 stars to Of Mice and Men), * SearchScreeningEvent (e.g. Check the showtimes for Wonder Woman in Paris). The training set contains of 13,084 utterances, the validation set and the test set contain 700 utterances each, with 100 queries per intent. Source: https://paperswithcode.com/paper/snips-voice-platform-an-embedded-spoken/

The PATIS is a Persian language dataset for intent detection and slot filling.

Dataset for our paper Disambiguation-Centric Finetuning Makes Enterprise Tool-Calling LLMs More Realistic and Less Risky which includes 5000 enterprise tools and the corresponding dialogues generated using DiaFORGE UTC data engine. The dataset is generated with the data generation engine. The engine simulates a user agent and an assistant agent in a dialogue, where the user agent has a persona and the assistant agent has access to a set of tools. Detailed information about the data generation process can be found in the paper. Each entry in the dataset contains: - seed: Gold tool for the generated dialogue. - user_persona: Persona of the simulated user agent. - messages: List of messages in the dialogue, where each message is a dictionary containing: - distractor_tools: List of distractor tools that are not the gold tool but are relevant to the dialogue. These tools are used by user agent to generate utterances that are hard to disambiguate from the gold tool. - retrieved_tools: List of tools retrieved by the assistant agent and used by the assistant agent in order to ask clarifying questions to the user agent.