Intent Classification
94 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
Benchmarks
These leaderboards are used to track progress in Intent Classification
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Libraries
Use these libraries to find Intent Classification models and implementationsDatasets
MNIST
SLURP
CLINC150
xSID
arXivEdits
Latest papers
ITALIC: An Italian Intent Classification Dataset
rita-nlp/italic
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Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects.
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training
We consider the task of few-shot intent detection, which involves training a deep learning model to classify utterances based on their underlying intents using only a small amount of labeled data.
Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification
amazon-science/intent-aware-encoder
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Intent classification (IC) plays an important role in task-oriented dialogue systems.
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
kinit-sk/crowd-vs-gpt-intent-class
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The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing?
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation
idiap/translation-aided-slu
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Motivated particularly by the task of cross-lingual SLU, we demonstrate that the task of speech translation (ST) is a good means of pretraining speech models for end-to-end SLU on both intra- and cross-lingual scenarios.
Improving End-to-End SLU performance with Prosodic Attention and Distillation
Most End-to-End SLU methods depend on the pretrained ASR or language model features for intent prediction.
ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development
tadeephuy/vimq
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Existing medical text datasets usually take the form of ques- tion and answer pairs that support the task of natural language gener- ation, but lacking the composite annotations of the medical terms.
CitePrompt: Using Prompts to Identify Citation Intent in Scientific Papers
For the ACL-ARC dataset, we report a 53. 86% F1 score for the zero-shot setting, which improves to 63. 61% and 66. 99% for the 5-shot and 10-shot settings, respectively.
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
lxk00/clap
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In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification.
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
NVIDIA/NeMo
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TDT models for Speech Recognition achieve better accuracy and up to 2. 82X faster inference than conventional Transducers.
