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
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.
Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition.
Ranked #3 on Intent Detection on ATIS
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on Named Entity Recognition on CMeEE
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.
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)
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.