Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

Recent developments in language modeling have enabled large text encoders to derive a
wealth of linguistic information from raw text corpora without supervision. Their success …

In this work, we focus on Cross-Lingual Event Detection where a model is trained on data
from a source language but its performance is evaluated on data from a second, target …

In this paper, we address the Event Detection task under a zero-shot cross-lingual setting
where a model is trained on a source language but evaluated on a distinct target language …

Political perspective detection has become an increasingly important task that can help
combat echo chambers and political polarization. Previous approaches generally focus on …

The neural revolution has redefined–and many would argue, undermined–the place of
traditional linguistics in natural language processing. The pace at which large unsupervised …

We study the problem of cross-lingual transfer learning for event detection (ED) where
models trained on a source language are expected to perform well on data for a new target …

Zero-shot information extraction (IE) tasks have attracted great attention recently. However,
how to jointly model multiple IE tasks in the zero-shot scenario is still an open question. In …

Knowledge-enhanced language models (KELMs) have emerged as promising tools to
bridge the gap between large-scale language models and domain-specific knowledge …

Event causality identification (ECI) aims to identify causal relations between events in texts.
Although existing event causality identification works based on fine-tuning pre-trained …