In recent years, the use of multi-modal pre-trained Transformers has led...
Optimizer is an essential component for the success of deep learning, wh...
BatGPT is a large-scale language model designed and trained jointly by W...
Universal Information Extraction (UIE) has been introduced as a unified
...
With the widespread use of large language models (LLMs) in NLP tasks,
re...
Masked Image Modeling (MIM) is a new self-supervised vision pre-training...
Commonsense reasoning is an appealing topic in natural language processi...
As a fundamental natural language processing task and one of core knowle...
Privacy protection is an important and concerning topic in Federated
Lea...
Aspect-based sentiment analysis (ABSA) task consists of three typical
su...
Unsupervised constituency parsing has been explored much but is still fa...
In this paper, we leverage pre-trained language models (PLMs) to precise...
Attention scorers have achieved success in parsing tasks like semantic a...
In Grammatical Error Correction (GEC), sequence labeling models enjoy fa...
Constituent and dependency parsing, the two classic forms of syntactic
p...
The task of semantic role labeling (SRL) is dedicated to finding the
pre...
Text encoding is one of the most important steps in Natural Language
Pro...
Semantic role labeling (SRL) aims at elaborating the meaning of a senten...
In natural language processing (NLP), cross-lingual transfer learning is...
In this paper, we introduced our joint team SJTU-NICT 's participation i...
Semantic role labeling is primarily used to identify predicates, argumen...
Standard neural machine translation (NMT) is on the assumption of
docume...
Semantic role labeling (SRL) is dedicated to recognizing the semantic
pr...
Exploiting common language as an auxiliary for better translation has a ...
State-of-the-art Transformer-based neural machine translation (NMT) syst...
Most syntactic dependency parsing models may fall into one of two catego...
The latest developments in neural semantic role labeling (SRL), includin...
Standard neural machine translation (NMT) is on the assumption of
docume...
Embedding from Language Models (ELMo) has shown to be effective for impr...
The latest work on language representations carefully integrates
context...
This work models named entity distribution from a way of visualizing
top...
Recently, semantic role labeling (SRL) has earned a series of success wi...
Both syntactic and semantic structures are key linguistic contextual clu...
Semantic role labeling (SRL) aims to discover the predicateargument stru...
Character-level representations have been broadly adopted to alleviate t...
The goal of semantic role labeling (SRL) is to discover the
predicate-ar...
Who did what to whom is a major focus in natural language understanding,...
Semantic role labeling (SRL) is to recognize the predicate-argument stru...