Instruction-tuning has become an integral part of training pipelines for...
Recent advances of powerful Language Models have allowed Natural Languag...
This report presents the results of the shared tasks organized as part o...
Manually annotated datasets are crucial for training and evaluating Natu...
Recently, various intermediate layer distillation (ILD) objectives have ...
The increasing number of benchmarks for Natural Language Processing (NLP...
In Natural Language Generation (NLG) tasks, for any input, multiple
comm...
Relation Extraction (RE) remains a challenging task, especially when
con...
Most research in Relation Extraction (RE) involves the English language,...
Transferring information retrieval (IR) models from a high-resource lang...
NLP datasets annotated with human judgments are rife with disagreements
...
One of the challenges with finetuning pretrained language models (PLMs) ...
Bilingual word lexicons are crucial tools for multilingual natural langu...
Despite much progress in recent years, the vast majority of work in natu...
Human variation in labeling is often considered noise. Annotation projec...
Calibration is a popular framework to evaluate whether a classifier know...
Linguistic information is encoded at varying timescales (subwords, phras...
With the increase in availability of large pre-trained language models (...
Relation Extraction (RE) has attracted increasing attention, but current...
Appropriate evaluation and experimental design are fundamental for empir...
Aggregated data obtained from job postings provide powerful insights int...
Making an informed choice of pre-trained language model (LM) is critical...
Skill Classification (SC) is the task of classifying job competences fro...
Over the last five years, research on Relation Extraction (RE) witnessed...
Skill Extraction (SE) is an important and widely-studied task useful to ...
The field of Deep Learning (DL) has undergone explosive growth during th...
Probing has become an important tool for analyzing representations in Na...
This work provides the first in-depth analysis of genre in Universal
Dep...
Recent work has shown that monolingual masked language models learn to
r...
We propose Cartography Active Learning (CAL), a novel Active Learning (A...
Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning...
This paper introduces DaN+, a new multi-domain corpus and annotation
gui...
De-identification is the task of detecting privacy-related entities in t...
The lack of publicly available evaluation data for low-resource language...
Recent complementary strands of research have shown that leveraging
info...
Citation count prediction is the task of predicting the number of citati...
Deep neural networks excel at learning from labeled data and achieve
sta...
Transfer learning, particularly approaches that combine multi-task learn...
This paper introduces FT Speech, a new speech corpus created from the
re...
Named Entity Recognition (NER) has greatly advanced by the introduction ...
We study the issue of catastrophic forgetting in the context of neural
m...
In natural language processing, the deep learning revolution has shifted...
We introduce DsDs: a cross-lingual neural part-of-speech tagger that lea...
Gender prediction has typically focused on lexical and social network
fe...
Novel neural models have been proposed in recent years for learning unde...
We present ALL-IN-1, a simple model for multilingual text classification...
Domain similarity measures can be used to gauge adaptability and select
...
Does normalization help Part-of-Speech (POS) tagging accuracy on noisy,
...
We propose UDP, the first training-free parser for Universal Dependencie...
Multitask learning has been applied successfully to a range of tasks, mo...