Existing knowledge-enhanced methods have achieved remarkable results in
...
We present Pre-trained Machine Reader (PMR), a novel method to retrofit
...
Neural machine translation (NMT) is often criticized for failures that h...
Relation extraction has the potential for large-scale knowledge graph
co...
Cross-lingual named entity recognition (NER) suffers from data scarcity ...
Due to the huge amount of parameters, fine-tuning of pretrained language...
Out-of-Domain (OOD) intent detection is important for practical dialog
s...
A wide range of control perspectives have been explored in controllable ...
Text-to-SQL parsing tackles the problem of mapping natural language ques...
In this paper, we propose a novel SQL guided pre-training framework STAR...
This paper introduces Doc2Bot, a novel dataset for building machines tha...
Recently, pre-training methods have shown remarkable success in task-ori...
Pre-training methods with contrastive learning objectives have shown
rem...
This paper aims to improve the performance of text-to-SQL parsing by
exp...
QA models with lifelong learning (LL) abilities are important for practi...
Text-to-SQL parsing is an essential and challenging task. The goal of
te...
The importance of building text-to-SQL parsers which can be applied to n...
Multi-modal document pre-trained models have proven to be very effective...
In this paper, we present Duplex Conversation, a multi-turn, multimodal
...
Prompt learning approaches have made waves in natural language processin...
Large-scale pretrained foundation models have been an emerging paradigm ...
Multimodal named entity recognition and relation extraction (MNER and MR...
Multimodal Knowledge Graphs (MKGs), which organize visual-text factual
k...
Pre-trained language models have contributed significantly to relation
e...
Traditionally, a debate usually requires a manual preparation process,
i...
Despite the importance of relation extraction in building and representi...
Pre-trained models have proved to be powerful in enhancing task-oriented...
Knowledge enriched language representation learning has shown promising
...
The Visual Question Answering (VQA) task utilizes both visual image and
...
Much recent progress in task-oriented dialogue (ToD) systems has been dr...
When directly using existing text generation datasets for controllable
g...
Most existing NER methods rely on extensive labeled data for model train...
Data augmentation for cross-lingual NER requires fine-grained control ov...
Artificial Intelligence (AI), along with the recent progress in biomedic...
Document-level relation extraction aims to extract relations among multi...
Adapter-based tuning has recently arisen as an alternative to fine-tunin...
Existing dialog state tracking (DST) models are trained with dialog data...
Large pre-trained language models achieve state-of-the-art results when
...
Ubiquitous internet access is reshaping the way we live, but it is
accom...
In this paper, we reformulate the relation extraction task as mask langu...
Cross-lingual word embeddings (CLWE) have been proven useful in many
cro...
Semantic parsing has long been a fundamental problem in natural language...
Data augmentation techniques have been widely used to improve machine
le...
Recent studies about learning multilingual representations have achieved...
Existing works on KG-to-text generation take as input a few RDF triples ...
Valentine Day February 14, is the day of love. The days ahead of Valenti...
Graph Convolutional Networks (GCNs) have gained significant developments...
Social media platforms have been used for information and news gathering...
Target-based sentiment analysis or aspect-based sentiment analysis (ABSA...
Online reviews provide rich information about products and service, whil...