Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

In the medical field, a doctor must have a comprehensive knowledge by reading and writing
narrative documents, and he is responsible for every decision he takes for patients …

Universally modeling all typical information extraction tasks (UIE) with one generative
language model (GLM) has revealed great potential by the latest study, where various IE …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Abstract Vision-language (V+ L) pretraining models have achieved great success in
supporting multimedia applications by understanding the alignments between images and …

General purpose relation extractors, which can model arbitrary relations, are a core
aspiration in information extraction. Efforts have been made to build general purpose …

Neural abstractive summarization models are prone to generate content inconsistent with
the source document, ie unfaithful. Existing automatic metrics do not capture such mistakes …

Neural abstractive summarization models are flexible and can produce coherent summaries,
but they are sometimes unfaithful and can be difficult to control. While previous studies …

Abstract Open-domain Question Answering models that directly leverage question-answer
(QA) pairs, such as closed-book QA (CBQA) models and QA-pair retrievers, show promise in …

Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples
into natural text, focused on domain-specific benchmark datasets. In this paper, however, we …