Intent Detection

110 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


Use these libraries to find Intent Detection models and implementations

Most implemented papers

BERT for Joint Intent Classification and Slot Filling

monologg/JointBERT 28 Feb 2019

Intent classification and slot filling are two essential tasks for natural language understanding.

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

DSKSD/RNN-for-Joint-NLU 6 Sep 2016

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.

Zero-shot User Intent Detection via Capsule Neural Networks

congyingxia/ZeroShotCapsule EMNLP 2018

User intent detection plays a critical role in question-answering and dialog systems.

Efficient Intent Detection with Dual Sentence Encoders

thuiar/textoir WS 2020

Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i. e., in few-shot setups).

Joint Slot Filling and Intent Detection via Capsule Neural Networks

czhang99/Capsule-NLU ACL 2019

Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

google/uncertainty-baselines 16 Jun 2021

Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks.

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

MiuLab/SlotGated-SLU NAACL 2018

Attention-based recurrent neural network models for joint intent detection and slot filling have achieved the state-of-the-art performance, while they have independent attention weights.

DELTA: A DEep learning based Language Technology plAtform

didi/delta 2 Aug 2019

In this paper we present DELTA, a deep learning based language technology platform.

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

LeePleased/StackPropagation-SLU IJCNLP 2019

In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge.