Previous work on crosslingual Relation and Event Extraction (REE) suffers from the
monolingual bias issue due to the training of models on only the source language data. An …

The identification of complex semantic structures such as events and entity relations, already
a challenging Information Extraction task, is doubly difficult from sources written in under …

Recent progress in cross-lingual relation and event extraction use graph convolutional
networks (GCNs) with universal dependency parses to learn language-agnostic sentence …

Transfer learning has shown promising results for transferring knowledge ofsource tasks to
target tasks in natural language processing (NLP). In this paper, we investigate a multi-task …

We address relation extraction as an analogy problem by proposing a novel approach to
learn representations of relations expressed by their textual mentions. In our assumption, if …

Understanding events entails recognizing the structural and temporal orders between event
mentions to build event structures/graphs for input documents. To achieve this goal, our …

Multi-lingual relation extraction aims to find unknown relational facts from text in various
languages. Existing models cannot well capture the consistency and diversity of relation …

Relation extraction (RE) is an important natural language processing task that predicts the
relation between two given entities, where a good understanding of the contextual …

In spite of the potential for ground-breaking achievements offered by large language models
(LLMs)(eg, GPT-3), they still lag significantly behind fully-supervised baselines (eg, fine …

Neural models have achieved remarkable success on relation extraction (RE) benchmarks.
However, there is no clear understanding which type of information affects existing RE …