Intent Detection

105 papers with code • 17 benchmarks • 20 datasets

Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.

Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers

Libraries

Use these libraries to find Intent Detection models and implementations

Latest papers with no code

RECIPE4U: Student-ChatGPT Interaction Dataset in EFL Writing Education

no code yet • 13 Mar 2024

RECIPE4U includes comprehensive records of these interactions, including conversation logs, students' intent, students' self-rated satisfaction, and students' essay edit histories.

Automatic driving lane change safety prediction model based on LSTM

no code yet • 28 Feb 2024

Autonomous driving technology can improve traffic safety and reduce traffic accidents.

Beyond the Known: Investigating LLMs Performance on Out-of-Domain Intent Detection

no code yet • 27 Feb 2024

Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems.

Intelligent Mode-switching Framework for Teleoperation

no code yet • 8 Feb 2024

User intention recognition is done at the operator side.

Designing deep neural networks for driver intention recognition

no code yet • 7 Feb 2024

We explore a pre-defined search space for three deep neural network layer types that are capable to handle sequential data (a long-short term memory, temporal convolution, and a time-series transformer layer), and the influence of different data fusion strategies on the driver intention recognition performance.

ICL Markup: Structuring In-Context Learning using Soft-Token Tags

no code yet • 12 Dec 2023

Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language.

SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU

no code yet • 16 Nov 2023

Task-oriented dialogue (ToD) systems help users execute well-defined tasks across a variety of domains (e. g., $\textit{flight booking}$ or $\textit{food ordering}$), with their Natural Language Understanding (NLU) components being dedicated to the analysis of user utterances, predicting users' intents ($\textit{Intent Detection}$, ID) and extracting values for informational slots ($\textit{Value Extraction}$, VE).

Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking

no code yet • 10 Nov 2023

Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains.

ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic

no code yet • 29 Oct 2023

This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain.

IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery

no code yet • 25 Oct 2023

Further, the intent classification may be modeled in a multiclass (MC) or multilabel (ML) setup.