Deep-Learning-Based Pre-training and Refined Tuning for Web Summarization Software
In the digital age, the rapid growth of web information has made it increasingly challenging
for individuals and organizations to effectively explore and extract valuable insights from the …
for individuals and organizations to effectively explore and extract valuable insights from the …
Claret: Pre-training a correlation-aware context-to-event transformer for event-centric generation and classification
Generating new events given context with correlated ones plays a crucial role in many event-
centric reasoning tasks. Existing works either limit their scope to specific scenarios or …
centric reasoning tasks. Existing works either limit their scope to specific scenarios or …
Tsvfn: Two-stage visual fusion network for multimodal relation extraction
Q Zhao, T Gao, N Guo - Information Processing & Management, 2023 - Elsevier
Multimodal relation extraction is a critical task in information extraction, aiming to predict the
class of relations between head and tail entities from linguistic sequences and related …
class of relations between head and tail entities from linguistic sequences and related …
Self-supervised logic induction for explainable fuzzy temporal commonsense reasoning
Understanding temporal commonsense concepts, such as times of occurrence and
durations is crucial for event-centric language understanding. Reasoning about such …
durations is crucial for event-centric language understanding. Reasoning about such …
KRACL: Contrastive learning with graph context modeling for sparse knowledge graph completion
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional
spaces and have become the de-facto standard for knowledge graph completion. Most …
spaces and have become the de-facto standard for knowledge graph completion. Most …
Multimodal event transformer for image-guided story ending generation
Image-guided story ending generation (IgSEG) is to generate a story ending based on given
story plots and ending image. Existing methods focus on cross-modal feature fusion but …
story plots and ending image. Existing methods focus on cross-modal feature fusion but …
Optimizing search advertising strategies: Integrating reinforcement learning with generalized second-price auctions for enhanced ad ranking and bidding
This paper explores the integration of strategic optimization methods in search advertising,
focusing on ad ranking and bidding mechanisms within E-commerce platforms. By …
focusing on ad ranking and bidding mechanisms within E-commerce platforms. By …
Enhancing Narrative Commonsense Reasoning With Multilevel Causal Knowledge
Narratives is an account of the unfolding of events, along with explanations of how and why
these processes and events came to be. To understand narratives, causality has been …
these processes and events came to be. To understand narratives, causality has been …
Improving cross-modal alignment for text-guided image inpainting
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in
a damaged image. Existing methods are based on a strong vision encoder and a cross …
a damaged image. Existing methods are based on a strong vision encoder and a cross …
Length-Aware Multi-Kernel Transformer for Long Document Classification
Lengthy documents pose a unique challenge to neural language models due to substantial
memory consumption. While existing state-of-the-art (SOTA) models segment long texts into …
memory consumption. While existing state-of-the-art (SOTA) models segment long texts into …