Intent Detection

88 papers with code • 15 benchmarks • 19 datasets

Intent Detection is a vital component of any task-oriented conversational system. In order to understand the user’s current goal, the system must leverage its intent detector to classify the user’s utterance (provided in varied natural language) into one of several predefined classes, that is, intents. However, the performance of intent detection has been hindered by the data scarcity issue, as it is non-trivial to collect sufficient examples for new intents. How to effectively identify user intents in few-shot learning has become popular.

Source: Few-shot Intent Detection Datasets, Baselines and Results

Source: Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection

Source: Efficient Intent Detection with Dual Sentence Encoders


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.