Papers Predicate Detection

VrdONE: One-stage Video Visual Relation Detection

Video Visual Relation Detection (VidVRD) focuses on understanding how entities interact over time and space in videos, a key step for gaining deeper insights into video scenes beyond basic visual tasks. Traditional metho…

Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction

We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD). Motivated by the effectiveness of Masked Image Modeling (MIM), we propose Masked …

Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection

The single-hidden-layer Randomly Weighted Feature Network (RWFN) introduced by Hong and Pavlic (2021) was developed as an alternative to neural tensor network approaches for relational learning tasks. Its relatively smal…

A Novel Metric for Evaluating Semantics Preservation

In this paper, we leverage pre-trained language models (PLMs) to precisely evaluate the semantics preservation of edition process on sentences. Our metric, Neighboring Distribution Divergence (NDD), evaluates the disturb…

Contextualized Semantic Distance between Highly Overlapped Texts

Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. Better evaluation of the semantic distance between the overlapped sentences benefit…

Comparison of single and multitask learning for predicting cognitive decline based on MRI data

The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia. Personalized prediction of the changes …

Weighted Training for Cross-Task Learning

In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks. We sh…

Linguistically-Informed Self-Attention for Semantic Role Labeling

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggestin…

Linguistically-Informed Self-Attention for Semantic Role Labeling

The current state-of-the-art end-to-end semantic role labeling (SRL) model is a deep neural network architecture with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramaticall…

Deep Semantic Role Labeling: What Works and What's Next

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM…

Verbal Valency Frame Detection and Selection in Czech and English

LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain

This paper presents Linked Health Answers, a natural language question answering systems that utilizes health data drawn from the Linked Data Cloud. The contributions of this paper are three-fold: Firstly, we review exis…