Intent Detection
114 papers with code • 17 benchmarks • 21 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 implementationsLatest papers with no code
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents
Existing research primarily addresses simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents and extracting different intent spans.
Intent Detection in the Age of LLMs
Intent detection is a critical component of task-oriented dialogue systems (TODS) which enables the identification of suitable actions to address user utterances at each dialog turn.
Benchmarking Adaptive Intelligence and Computer Vision on Human-Robot Collaboration
Additionally, our SLB mechanism achieves a labeling accuracy of 91%, reducing a significant amount of time that would've been spent on manual annotation.
Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises
Deep learning models, such as those used for face recognition and attribute prediction, are susceptible to manipulations like adversarial noise and unintentional noise, including Gaussian and impulse noise.
Experimental Evaluation of Machine Learning Models for Goal-oriented Customer Service Chatbot with Pipeline Architecture
Specifically, we focus on optimizing hyperparameters and evaluating candidate models for NLU (utilizing BERT and LSTM), DM (employing DQN and DDQN), and NLG (leveraging GPT-2 and DialoGPT).
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
The framework includes four distinctive tasks: Key Event Recognition, Mixed Event Recognition, Implicit Emotional Recognition, and Intention Recognition.
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection
In the realm of task-oriented dialogue systems, a robust intent detection mechanism must effectively handle malformed utterances encountered in real-world scenarios.
MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU
This paper introduces a novel approach, MIDAS, leveraging a multi-level intent, domain, and slot knowledge distillation for multi-turn NLU.
Interpretation of the Intent Detection Problem as Dynamics in a Low-dimensional Space
This space is constrained to a low-dimensional manifold whose dimensionality is related to the embedding and hidden layer sizes.
Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware
Objective: This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data.