Intent Classification
94 papers with code • 5 benchmarks • 13 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
Benchmarks
These leaderboards are used to track progress in Intent Classification
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Libraries
Use these libraries to find Intent Classification models and implementationsDatasets
MNIST
SLURP
CLINC150
xSID
arXivEdits
Most implemented papers
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
malllabiisc/DiPS
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NAACL 2019
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
We present Emu, a system that semantically enhances multilingual sentence embeddings.
Reconstructing Capsule Networks for Zero-shot Intent Classification
fanolabs/0shot-classification
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IJCNLP 2019
With the burgeoning of conversational AI, existing systems are not capable of handling numerous fast-emerging intents, which motivates zero-shot intent classification.
Metric Learning for Dynamic Text Classification
asappresearch/dynamic-classification
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WS 2019
However, in many real-world applications the label set is frequently changing.
Interactive Classification by Asking Informative Questions
asappresearch/interactive-classification
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ACL 2020
We study the potential for interaction in natural language classification.
Fast Intent Classification for Spoken Language Understanding
To address the latency and computational complexity issues, we explore a BranchyNet scheme on an intent classification scheme within SLU systems.
Stacked DeBERT: All Attention in Incomplete Data for Text Classification
This is due to the fact that current approaches are built for and trained with clean and complete data, and thus are not able to extract features that can adequately represent incomplete data.
Intent Classification in Question-Answering Using LSTM Architectures
Question-answering (QA) is certainly the best known and probably also one of the most complex problem within Natural Language Processing (NLP) and artificial intelligence (AI).
MTSI-BERT: A Session-aware Knowledge-based Conversational Agent
seo-95/MTSI-BERT
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LREC 2020
In the last years, the state of the art of NLP research has made a huge step forward.
ImpactCite: An XLNet-based method for Citation Impact Analysis
DominiqueMercier/ImpactCite
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Therefore, citation impact analysis (which includes sentiment and intent classification) enables us to quantify the quality of the citations which can eventually assist us in the estimation of ranking and impact.
