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
A Persian Benchmark for Joint Intent Detection and Slot Filling
To evaluate the effectiveness of our benchmark, we employ state-of-the-art methods for intent detection and slot filling.
Multi-Tenant Optimization For Few-Shot Task-Oriented FAQ Retrieval
Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis-\`a-vis community based FAQs.
Effectiveness of Text, Acoustic, and Lattice-based representations in Spoken Language Understanding tasks
In this paper, we perform an exhaustive evaluation of different representations to address the intent classification problem in a Spoken Language Understanding (SLU) setup.
Learning to Select from Multiple Options
jiangshdd/learningtoselect
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To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.
Deep Smart Contract Intent Detection
Nowadays, security activities in smart contracts concentrate on vulnerability detection.
A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding
zhihong-zhu/dgif
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Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios.
DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection
While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance.
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the \textit{mutual guidances} between the two tasks.
A Unified Framework for Multi-intent Spoken Language Understanding with prompting
Multi-intent Spoken Language Understanding has great potential for widespread implementation.
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task
phuongnm94/JointBERT_CAE
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Advances in Computational Collective Intelligence 2022
In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance.
