Intent Detection
106 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
Libraries
Use these libraries to find Intent Detection models and implementationsLatest papers
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
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
On the other side, a labeled dataset is needed to train a model for Intent Detection in task-oriented dialogue systems.
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
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.