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 Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
The developed multi-source UDA theory is theoretical and the generalization error on target subject is guaranteed.
Intent Detection and Entity Extraction from BioMedical Literature
Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature.
BlendX: Complex Multi-Intent Detection with Blended Patterns
Task-oriented dialogue (TOD) systems are commonly designed with the presumption that each utterance represents a single intent.
Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction
SJY8460/Uni-MIS
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Proceedings of the AAAI Conference on Artificial Intelligence 2024
In this work, we present a novel architecture by modeling the multi-intent SLU as a multi-view intent-slot interaction.
SDIF-DA: A Shallow-to-Deep Interaction Framework with Data Augmentation for Multi-modal Intent Detection
The two core challenges for multi-modal intent detection are (1) how to effectively align and fuse different features of modalities and (2) the limited labeled multi-modal intent training data.
JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention
JPIS incorporates the supporting profile information into its encoder and introduces a slot-to-intent attention mechanism to transfer slot information representations to intent detection.
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations.
SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU
Task-oriented dialogue (ToD) systems help users execute well-defined tasks across a variety of domains (e. g., $\textit{flight booking}$ or $\textit{food ordering}$), with their Natural Language Understanding (NLU) components being dedicated to the analysis of user utterances, predicting users' intents ($\textit{Intent Detection}$, ID) and extracting values for informational slots ($\textit{Value Extraction}$, VE).
Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models
stoyian/OCaTS
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We propose a framework that allows reducing the calls to LLMs by caching previous LLM responses and using them to train a local inexpensive model on the SME side.
Intent Detection and Slot Filling for Home Assistants: Dataset and Analysis for Bangla and Sylheti
As voice assistants cement their place in our technologically advanced society, there remains a need to cater to the diverse linguistic landscape, including colloquial forms of low-resource languages.
