Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Relation extraction (RE) involves identifying the relations between entities from unstructured
texts. RE serves as the foundation for many natural language processing (NLP) applications …

Task transfer, transferring knowledge contained in related tasks, holds the promise of
reducing the quantity of labeled data required to fine-tune language models. Dialogue …

State-of-the-art task-oriented dialogue systems typically rely on task-specific ontologies for
fulfilling user queries. The majority of task-oriented dialogue data, such as customer service …

Abstract Knowledge graphs are important in human-centered AI because of their ability to
reduce the need for large labelled machine-learning datasets, facilitate transfer learning …

The goal of dialogue relation extraction (DRE) is to identify the relation between two entities
in a given dialogue. During conversations, speakers may expose their relations to certain …

Advances in conversational AI systems, powered in particular by large language models,
have facilitated rapid progress in understanding and generating dialog. Typically, task …

Knowledge graphs are important in human-centered AI because of their ability to reduce the
need for large labelled machine-learning datasets, facilitate transfer learning, and generate …

Developing dialogue relation extraction (DRE) systems often requires a large amount of
labeled data, which can be costly and time-consuming to annotate. In order to improve …