Intent Detection
109 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 with no code
IntenDD: A Unified Contrastive Learning Approach for Intent Detection and Discovery
Further, the intent classification may be modeled in a multiclass (MC) or multilabel (ML) setup.
IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization
Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services.
Key-phrase boosted unsupervised summary generation for FinTech organization
Some of the NLP applications such as intent detection, sentiment classification, text summarization can help FinTech organizations to utilize the social media language data to find useful external insights and can be further utilized for downstream NLP tasks.
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction
Automatic speech recognition (ASR) systems often encounter difficulties in accurately recognizing rare words, leading to errors that can have a negative impact on downstream tasks such as keyword spotting, intent detection, and text summarization.
I$^2$KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding
Despite of the success of existing zero-shot cross-lingual SLU models, most of them neglect to achieve the mutual guidance between intent and slots.
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy.
ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education
We analyze students' usage patterns and perceptions regarding generative AI with respect to their intent and satisfaction.
Intent Detection at Scale: Tuning a Generic Model using Relevant Intents
Furthermore, we propose a strategy for using the clients relevant intents as model features that proves to be resilient to changes in the relevant intents of clients -- a common occurrence in production environments.
Continual Learning with Dirichlet Generative-based Rehearsal
Recent advancements in data-driven task-oriented dialogue systems (ToDs) struggle with incremental learning due to computational constraints and time-consuming issues.
Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection
Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents.
