Intent Detection

28 papers with code · Natural Language Processing

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.

Source: Efficient Intent Detection with Dual Sentence Encoders github.com/PolyAI-LDN/polyai-models

Benchmarks

Greatest papers with code

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

NAACL 2018 MiuLab/SlotGated-SLU

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.

INTENT DETECTION SLOT FILLING SPOKEN DIALOGUE SYSTEMS SPOKEN LANGUAGE UNDERSTANDING

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

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

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

INTENT CLASSIFICATION INTENT DETECTION SLOT FILLING

Joint Slot Filling and Intent Detection via Capsule Neural Networks

ACL 2019 czhang99/Capsule-NLU

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

INTENT DETECTION NATURAL LANGUAGE UNDERSTANDING SLOT FILLING

TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue

15 Apr 2020jasonwu0731/ToD-BERT

The underlying difference of linguistic patterns between general text and task-oriented dialogue makes existing pre-trained language models less useful in practice.

DIALOGUE STATE TRACKING INTENT DETECTION LANGUAGE MODELLING NATURAL LANGUAGE UNDERSTANDING

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

IJCNLP 2019 LeePleased/StackPropagation-SLU

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.

INTENT DETECTION SLOT FILLING SPOKEN LANGUAGE UNDERSTANDING

Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals

25 Jun 2020SuperBruceJia/EEG-DL

In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.

EEG INTENT DETECTION SEIZURE PREDICTION