Transfer learning, particularly approaches that combine multi-task learning with pre-trained
contextualized embeddings and fine-tuning, have advanced the field of Natural Language …

Word embeddings and pre-trained language models allow to build rich representations of
text and have enabled improvements across most NLP tasks. Unfortunately they are very …

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal
multiword expressions. We present the annotation methodology, focusing on changes from …

The world's languages exhibit striking diversity. At the same time, recurring linguistic
patterns suggest the possibility that this diversity is shaped by features of human cognition …

Sequential neural networks models are powerful tools in a variety of Natural Language
Processing (NLP) tasks. The sequential nature of these models raises the questions: to what …

This paper describes the changes applied to the original process used to convert the Index
Thomisticus Treebank, a corpus including texts in Medieval Latin by Thomas Aquinas, into …

HITS participated in the Discourse Segmentation (DS, Task 1) and Connective Detection
(CD, Task 2) tasks at the DISRPT 2023. Task 1 focuses on segmenting the text into …

Probing has become an important tool for analyzing representations in Natural Language
Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are …

In this paper we describe the participation of the WordUp! team in the VaxxStance shared
task at IberLEF 2021. The goal of the competition is to determine the author's stance from …

Discourse parsing, which involves understanding the structure, information flow, and
modeling the coherence of a given text, is an important task in natural language processing …