Intent Classification
95 papers with code • 4 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
Most implemented papers
Diverse Few-Shot Text Classification with Multiple Metrics
Gorov/DiverseFewShot_Amazon
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NAACL 2018
We study few-shot learning in natural language domains.
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
Kaleidophon/deep-significance
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NAACL 2021
To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
cbluebenchmark/cblue
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ACL 2022
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
CAPE: Context-Aware Private Embeddings for Private Language Learning
Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models
In Spoken Language Understanding (SLU) the task is to extract important information from audio commands, like the intent of what a user wants the system to do and special entities like locations or numbers.
Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection
gt4sd/zero-shot-bert-adapters
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In our evaluation, we first analyze the quality of the model after adaptive fine-tuning on known classes.
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?
Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
zzz47zzz/codebase-for-incremental-learning-with-llm
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Most assume that catastrophic forgetting is the biggest obstacle to achieving superior IL performance and propose various techniques to overcome this issue.
Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue system
Perevalov/intent_classifier
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The subject area of our system is very specific, that is why there is a lack of training data.
