intent-classification
89 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in intent-classification
MASSIVE
Intent-based user instruction for electric automation
Most implemented papers
Diverse Few-Shot Text Classification with Multiple Metrics
Gorov/DiverseFewShot_Amazon
•
•
NAACL 2018
We study few-shot learning in natural language domains.
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding
Kaleidophon/deep-significance
•
•
NAACL 2021
To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer.
CAPE: Context-Aware Private Embeddings for Private Language Learning
Deep learning-based language models have achieved state-of-the-art results in a number of applications including sentiment analysis, topic labelling, intent classification and others.
Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems.
Z-BERT-A: a zero-shot Pipeline for Unknown Intent detection
gt4sd/zero-shot-bert-adapters
•
•
In our evaluation, we first analyze the quality of the model after adaptive fine-tuning on known classes.
ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness
kinit-sk/crowd-vs-gpt-intent-class
•
•
The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing?
Question Embeddings Based on Shannon Entropy: Solving intent classification task in goal-oriented dialogue system
Perevalov/intent_classifier
•
•
The subject area of our system is very specific, that is why there is a lack of training data.
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.
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.
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization
We present Emu, a system that semantically enhances multilingual sentence embeddings.
