The immense evolution in Large Language Models (LLMs) has underscored th...
LLMs have demonstrated great capabilities in various NLP tasks. Differen...
Large language models (LLMs) have emerged as a new paradigm for Text-to-...
This work summarizes two strategies for completing time-series (TS) task...
Despite the superior performance, Large Language Models (LLMs) require
s...
Despite substantial progress in abstractive text summarization to genera...
Federated Learning (FL) aims to train machine learning models for multip...
Numerous research studies in the field of federated learning (FL) have
a...
In recent years, a plethora of spectral graph neural networks (GNN) meth...
Federated Learning (FL) aims to train high-quality models in collaborati...
In this work, besides improving prediction accuracy, we study whether
pe...
The increasing privacy concerns on personal private text data promote th...
To develop effective and efficient graph similarity learning (GSL) model...
Big data processing at the production scale presents a highly complex
en...
In order to develop effective sequential recommenders, a series of seque...
Hyperparameter optimization (HPO) is crucial for machine learning algori...
Personalized Federated Learning (pFL), which utilizes and deploys distin...
To investigate the heterogeneity of federated learning in real-world
sce...
Recently, sequential recommendation has emerged as a widely studied topi...
Graph Neural Networks (GNNs) have received extensive research attention ...
The incredible development of federated learning (FL) has benefited vari...
Although remarkable progress has been made by the existing federated lea...
Recently, Product Question Answering (PQA) on E-Commerce platforms has
a...
On many natural language processing tasks, large pre-trained language mo...
Despite significant progress has been achieved in text summarization, fa...
Owing to the remarkable capability of extracting effective graph embeddi...
Texts convey sophisticated knowledge. However, texts also convey sensiti...
Conversational recommender systems (CRS) enable the traditional recommen...
Answer selection, which is involved in many natural language processing
...
Despite pre-trained language models such as BERT have achieved appealing...
In recent years, conversational recommender system (CRS) has received mu...
Database indexes facilitate data retrieval and benefit broad application...
Pre-trained language models have been applied to various NLP tasks with
...
The literature has witnessed the success of applying deep Transfer Learn...
In recent years, there are a large number of recommendation algorithms
p...
To automate the generation of interactive features, recent methods are
p...
Graph convolutional networks (GCNs) are a powerful deep learning approac...
Recently, deep learning has made significant progress in the task of
seq...
Distant supervision based methods for entity and relation extraction hav...
Online recommendation systems make use of a variety of information sourc...
Causal inference is a critical research topic across many domains, such ...
Large pre-trained language models such as BERT have shown their effectiv...
In order to efficiently learn with small amount of data on new tasks,
me...
Community question answering (CQA) gains increasing popularity in both
a...
This paper presents a novel framework, MGNER, for Multi-Grained Named En...
Knowledge graph embedding (KGE) is a technique for learning continuous
e...
Being able to automatically discover synonymous entities from a large
fr...
Being able to recognize words as slots and detect the intent of an utter...
Answer selection and knowledge base question answering (KBQA) are two
im...
Question answering (QA) has achieved promising progress recently. Howeve...