64 papers with code • 1 benchmarks • 1 datasets
These leaderboards are used to track progress in intent-classification
Most implemented papers
BERT for Joint Intent Classification and Slot Filling
Intent classification and slot filling are two essential tasks for natural language understanding.
Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.
Benchmarking Natural Language Understanding Services for building Conversational Agents
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.
Induction Networks for Few-Shot Text Classification
Therefore, we should be able to learn a general representation of each class in the support set and then compare it to new queries.
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries.
ConveRT: Efficient and Accurate Conversational Representations from Transformers
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.
Subword Semantic Hashing for Intent Classification on Small Datasets
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.
The First Evaluation of Chinese Human-Computer Dialogue Technology
In this paper, we introduce the first evaluation of Chinese human-computer dialogue technology.
Diverse Few-Shot Text Classification with Multiple Metrics
We study few-shot learning in natural language domains.
End-to-End Slot Alignment and Recognition for Cross-Lingual NLU
We introduce MultiATIS++, a new multilingual NLU corpus that extends the Multilingual ATIS corpus to nine languages across four language families, and evaluate our method using the corpus.