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
Most implemented papers
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
ray075hl/Bi-Model-Intent-And-Slot
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NAACL 2018
The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model.
Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue system
Perevalov/intent_classifier
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The subject area of our system is very specific, that is why there is a lack of training data.
Spoken Language Intent Detection using Confusion2Vec
In this paper, we address the spoken language intent detection under noisy conditions imposed by automatic speech recognition (ASR) systems.
Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts
Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image.
Deep Unknown Intent Detection with Margin Loss
With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance.
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation
In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions.
Attention-Informed Mixed-Language Training for Zero-shot Cross-lingual Task-oriented Dialogue Systems
zliucr/mixed-language-training
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Recently, data-driven task-oriented dialogue systems have achieved promising performance in English.
“Where is My Parcel?” Fast and Efficient Classifiers to Detect User Intent in Natural Language
fabon/chatbot-intents
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SNAMS 2019
We study the performance of customer intent classifiers designed to predict the most popular intent received through ASOS. com Customer Care Department, namely “Where is my order?”.
User-in-the-loop Adaptive Intent Detection for Instructable Digital Assistant
To provide such functionalities, NL interpretation in traditional assistants should be improved: (1) The intent identification system should be able to recognize new forms of known intents, and to acquire new intents as they are expressed by the user.
Sequence Labeling Approach to the Task of Sentence Boundary Detection
One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances.
