Intent Classification

33 papers with code • 1 benchmarks • 6 datasets

Intent Classification is the task of correctly labeling a natural language utterance from a predetermined set of intents

Source: Multi-Layer Ensembling Techniques for Multilingual Intent Classification

Greatest papers with code

Benchmarking Natural Language Understanding Services for building Conversational Agents

RasaHQ/rasa 13 Mar 2019

We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible to the lay developer.

General Classification Intent Classification +1

BERT for Joint Intent Classification and Slot Filling

monologg/JointBERT 28 Feb 2019

Intent classification and slot filling are two essential tasks for natural language understanding.

General Classification Intent Classification +2

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

DSKSD/RNN-for-Joint-NLU 6 Sep 2016

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.

Intent Classification Intent Detection +1

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

CBLUEbenchmark/CBLUE 15 Jun 2021

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.

Intent Classification Medical Concept Normalization +6

Example-Driven Intent Prediction with Observers

alexa/dialoglue NAACL 2021

Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances.

Intent Classification Sentence Similarity

Subword Semantic Hashing for Intent Classification on Small Datasets

kumar-shridhar/Know-Your-Intent 16 Oct 2018

In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks.

Chatbot General Classification +3

An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction

clinc/oos-eval IJCNLP 2019

We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries.

General Classification Intent Classification +1

Structural Scaffolds for Citation Intent Classification in Scientific Publications

allenai/scicite NAACL 2019

Identifying the intent of a citation in scientific papers (e. g., background information, use of methods, comparing results) is critical for machine reading of individual publications and automated analysis of the scientific literature.

 Ranked #1 on Citation Intent Classification on ACL-ARC (using extra training data)

Citation Intent Classification General Classification +3

Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation

malllabiisc/DiPS NAACL 2019

Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.

Data Augmentation Intent Classification