Intent Detection
110 papers with code • 17 benchmarks • 20 datasets
Intent Detection is a task of determining the underlying purpose or goal behind a user's search query given a context. The task plays a significant role in search and recommendations. A traditional approach for intent detection implies using an intent detector model to classify user search query into predefined intent categories, given a context. One of the key challenges of the task implies identifying user intents for cold-start sessions, i.e., search sessions initiated by a non-logged-in or unrecognized user.
Source: Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers
Benchmarks
These leaderboards are used to track progress in Intent Detection
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Libraries
Use these libraries to find Intent Detection models and implementations
ATIS
SNIPS
BANKING77
CLINC150
Latest papers
RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training
tommytyc/rsvp
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Existing intent detection approaches have highly relied on adaptively pre-training language models with large-scale datasets, yet the predominant cost of data collection may hinder their superiority.
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
The results also demonstrate the contributions of both bidirectional design and the training method to the accuracy improvement.
ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
fangyihao/gptaug
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Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text.
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
nhhoang96/multicl_slot_induction
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Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e. g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance.
ReCoMIF: Reading comprehension based multi-source information fusion network for Chinese spoken language understanding
It usually includes slot filling and intent detection (SFID) tasks aiming at semantic parsing of utterances.
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.
Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets
We present a novel tri-level joint natural language understanding approach, adding domain, and explicitly exchange semantic information between all levels.
Improved Instruction Ordering in Recipe-Grounded Conversation
In this paper, we study the task of instructional dialogue and focus on the cooking domain.
CTRAN: CNN-Transformer-based Network for Natural Language Understanding
rafiepour/CTran
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For the intent-detection decoder, we utilize self-attention followed by a linear layer.
A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery
Makbari1997/VAE-KPCA-HDBSCAN
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On the other side, a labeled dataset is needed to train a model for Intent Detection in task-oriented dialogue systems.
