Intent Classification

A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets. Source: SLURP: A Spoken Language Understanding Resource Package

This dataset is for evaluating the performance of intent classification systems in the presence of "out-of-scope" queries, i.e., queries that do not fall into any of the system-supported intent classes. The dataset includes both in-scope and out-of-scope data. Source: CLINC150

MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations for the Natural Language Understanding tasks of intent prediction and slot annotation. Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions.

xSID, a new evaluation benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect, covering Arabic (ar), Chinese (zh), Danish (da), Dutch (nl), English (en), German (de), Indonesian (id), Italian (it), Japanese (ja), Kazakh (kk), Serbian (sr), Turkish (tr) and an Austro-Bavarian German dialect, South Tyrolean (de-st).

KUAKE Query Intent Classification, a dataset for intent classification, is used for the KUAKE-QIC task. Given the queries of search engines, the task requires to classify each of them into one of 11 medical intent categories defined in KUAKE-QIC, including diagnosis, etiology analysis, treatment plan, medical advice, test result analysis, disease description, consequence prediction, precautions, intended effects, treatment fees, and others.

A dataset with a single banking domain, includes both general Out-of-Scope (OOD-OOS) queries and In-Domain but Out-of-Scope (ID-OOS) queries, where ID-OOS queries are semantically similar intents/queries with in-scope intents. BANKING77 originally includes 77 intents. BANKING77-OOS includes 50 in-scope intents in this dataset, and the ID-OOS queries are built up based on 27 held-out in-scope intents. Conduct intent detection with and without OOD-OOS and ID-OOS queries

A dataset with two separate domains, i.e., the "Banking'' domain and the "Credit cards'' domain with both general Out-of-Scope (OOD-OOS) queries and In-Domain but Out-of-Scope (ID-OOS) queries, where ID-OOS queries are semantically similar intents/queries with in-scope intents. Each domain in CLINC150 originally includes 15 intents. Each domain includes ten in-scope intents in this dataset, and the ID-OOS queries are built up based on five held-out in-scope intents. Can be used to conduct intent detection with and without OOD-OOS and ID-OOS queries

ViMQ is a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations for the Intent Classification and Named Entity Recognition tasks. It contains Vietnamese medical questions crawled from the consultation section online between patients and doctors from www.vinmec.com, a website of a Vietnamese general hospital. Each consultation consists of a question regarding a specific health issue of a patient and a detailed respond provided by a clinical expert. The dataset contains health issues that fall into a wide range of categories including common illness, cardiology, hematology, cancer, pediatrics, etc. We removed sections where users ask about information of the hospital and selected 9,000 questions for the dataset. Source: ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development

arXivEdits an annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1,000 sentence pairs. This dataset is designed for studying the human revision process in the scientific writing domain. Source: arXivEdits: Understanding the Human Revision Process in Scientific Writing Image Source: https://arxiv.org/pdf/2210.15067v1.pdf

A labelled version of the ORCAS click-based dataset of Web queries, which provides 18 million connections to 10 million distinct queries. DOI of the dataset: 10.48436/pp7xz-n9a06

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.

A novel corpus of 15,356 Polish web articles, including articles identified as containing disinformation. Our dataset enables a multifaceted understanding of disinformation. We present a distinctive multilayered methodology for annotating disinformation in texts. What sets our corpus apart is its focus on uncovering hidden intent and manipulation in disinformative content. A team of experts annotated each article with multiple labels indicating both disinformation creators’ intents and the manipulation techniques employed.

Search4Code is a large-scale web query based dataset of code search queries for C# and Java. The Search4Code data is mined from Microsoft Bing's anonymized search query logs using weak supervision technique. Source: https://github.com/microsoft/Search4Code

This dataset for Intent classification from human speech covers 14 coarse-grained intents from the Banking domain. This work is inspired by a similar release in the Minds-14 dataset - here, we restrict ourselves to Indian English but with a much larger training set. The data was generated by 11 (Indian English) speakers and recorded over a telephony line. We also provide access to anonymized speaker information - like gender, languages spoken, and native language - to allow more structured discussions around robustness and bias in the models you train. Source: Skit-S2I: An Indian Accented Speech to Intent dataset