Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Task-oriented dialogue (TOD) systems are required to identify key information from
conversations for the completion of given tasks. Such information is conventionally specified …

Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source
domain to the unlabeled target domain. Existing models either encode slot descriptions and …

Manually annotating fine-grained slot-value labels for task-oriented dialogue (ToD) systems
is an expensive and time-consuming endeavour. This motivates research into slot-filling …

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for
enhancing the adaptability and robustness of conversational agents. Traditional models rely …

Task-oriented parsing (TOP) aims to convert natural language into machine-readable
representations of specific tasks, such as setting an alarm. A popular approach to TOP is to …

We present a human-in-the-loop evaluation framework for fact-checking novel
misinformation claims and identifying social media messages that support them. Our …

We propose a new knowledge representation (KR) based on knowledge bases (KBs)
derived from text, based on question generation and entity linking. We argue that the …

This paper addresses zero-shot slot filling, which tries to build a system that can generalize
to unseen slot types without any training data. The key to zero-shot slot-filling is to match the …