Most machine-learning methods focus on classifying instances whose classes have already
been seen in training. In practice, many applications require classifying instances whose …

User intent detection plays a critical role in question-answering and dialog systems. Most
previous works treat intent detection as a classification problem where utterances are …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

Recently, large pretrained language models have demonstrated strong language
understanding capabilities. This is particularly reflected in their zero-shot and in-context …

Task-oriented dialog systems increasingly rely on deep learning-based slot filling models,
usually needing extensive labeled training data for target domains. Often, however, little to …

The MITRE Corporation is a non-profit organization that has made substantial efforts into
creating and maintaining knowledge bases relevant to cybersecurity and has been widely …

User intent classification plays a vital role in dialogue systems. Since user intent may
frequently change over time in many realistic scenarios, unknown (new) intent detection has …

Human-Computer dialogue systems provide a natural language based interface between
human and computers. They are widely demanded in network information services …

Neural text classification models typically treat output labels as categorical variables that
lack description and semantics. This forces their parametrization to be dependent on the …

Neural headline generation (NHG) has been proven to be effective in generating a fully
abstractive headline recently. Existing NHG systems are only capable of producing headline …