Document-level relation extraction aims to identify relationships betwee...
Diffusion models have emerged as a powerful paradigm for generation,
obt...
Existing metrics for evaluating the quality of automatically generated
q...
We present a detailed privacy analysis of Samsung's Offline Finding (OF)...
In recent years, multilingual machine translation models have achieved
p...
We propose a VAE for Transformers by developing a variational informatio...
Recently, very large pre-trained models achieve state-of-the-art results...
Current methods for few-shot fine-tuning of pretrained masked language m...
The state-of-the-art models for coreference resolution are based on
inde...
Transformer-based architectures are the model of choice for natural lang...
Text autoencoders are often used for unsupervised conditional text gener...
Though language model text embeddings have revolutionized NLP research, ...
We describe the DCU-EPFL submission to the IWPT 2021 Shared Task on Pars...
While large-scale pretrained language models have obtained impressive re...
Adapting large-scale pretrained language models to downstream tasks via
...
State-of-the-art parameter-efficient fine-tuning methods rely on introdu...
In this paper, we propose a new approach to infer state machine models f...
The goal of semantic role labelling (SRL) is to recognise the
predicate-...
Characters do not convey meaning, but sequences of characters do. We pro...
Generative adversarial networks (GANs) have succeeded in inducing
cross-...
Text autoencoders are commonly used for conditional generation tasks suc...
In this paper, we trace the history of neural networks applied to natura...
We propose the Recursive Non-autoregressive Graph-to-graph Transformer
a...
Transition-based dependency parsing is a challenging task for conditioni...
There have been several studies recently showing that strong natural lan...
Learning to detect entity mentions without using syntactic information c...
Neural language modeling (LM) has led to significant improvements in sev...
Many tasks, including language generation, benefit from learning the
str...
Distributed representations of words which map each word to a continuous...
This paper demonstrates that word sense disambiguation (WSD) can improve...
Neural Machine Translation (NMT) can be improved by including document-l...
Tying the weights of the target word embeddings with the target word
cla...
Neural text classification methods typically treat output classes as
cat...
Lexical entailment, such as hyponymy, is a fundamental issue in the sema...
Vector-space models, from word embeddings to neural network parsers, hav...
Distributional semantics creates vector-space representations that captu...
We propose a Bayesian model of unsupervised semantic role induction in
m...
Previous work has shown the effectiveness of random walk hitting times a...