Intent Classification
91 papers with code • 5 benchmarks • 13 datasets
Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents
Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification
Libraries
Use these libraries to find Intent Classification models and implementationsDatasets
Latest papers with no code
LARA: Linguistic-Adaptive Retrieval-Augmented LLMs for Multi-Turn Intent Classification
Following the significant achievements of large language models (LLMs), researchers have employed in-context learning for text classification tasks.
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature.
Prompt Perturbation Consistency Learning for Robust Language Models
However, their performance on sequence labeling tasks such as intent classification and slot filling (IC-SF), which is a central component in personal assistant systems, lags significantly behind discriminative models.
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text.
PerSHOP -- A Persian dataset for shopping dialogue systems modeling
In this paper, we developed a dataset of dialogues in the Persian language through crowd-sourcing.
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System
Furthermore, to glean a nuanced understanding of OmniDialog's strengths and potential pitfalls, we designed a fine-grained analysis framework for dialogue-centric tasks.
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available.
Bengali Intent Classification with Generative Adversarial BERT
Furthermore, we propose a novel approach for Bengali intent classification using Generative Adversarial BERT to evaluate the proposed dataset, which we call GAN-BnBERT.
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings
We build on existing light models for intent classification in Flemish, and our main contribution is applying different augmentation techniques on two levels -- the voice level, and the phonetic transcripts level -- to the existing models to counter the problem of scarce labeled data in low-resource languages.
Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution
In the quest for efficient neural network models for neural data interpretation and user intent classification in brain-computer interfaces (BCIs), learning meaningful sparse representations of the underlying neural subspaces is crucial.