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
Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
In our experiments, using graph-guided response simulations leads to significant improvements in intent classification, slot filling and response relevance compared to naive single-prompt simulated conversations.
New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark
A combination ofmultiple datasets, including the MEDIA dataset, was suggested for training this joint model.
ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler
State-of-the-art intent classification (IC) and slot filling (SF) methods often rely on data-intensive deep learning models, limiting their practicality for industry applications.
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification
frank7li/generating-hard-negative-out-of-scope-data-with-chatgpt-for-intent-classification
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Intent classifiers must be able to distinguish when a user's utterance does not belong to any supported intent to avoid producing incorrect and unrelated system responses.
Augmenting Automation: Intent-Based User Instruction Classification with Machine Learning
Our system represents user instructions as intents, allowing for dynamic control of electrical circuits without relying on predefined commands.
Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
zzz47zzz/pretrained-lm-for-incremental-learning
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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.
Dense Retrieval as Indirect Supervision for Large-space Decision Making
Many discriminative natural language understanding (NLU) tasks have large label spaces.
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
In the present work, we describe Top-K K-Nearest Neighbor (TK-KNN), which uses a more robust pseudo-labeling approach based on distance in the embedding space while maintaining a balanced set of pseudo-labeled examples across classes through a ranking-based approach.
InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
We present InstructTODS, a novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue systems that can adapt to diverse domains without fine-tuning.
Conversational Financial Information Retrieval Model (ConFIRM)
With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration.
