Search Results for author:
Found 34 papers, 17 papers with code
PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering
Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly those dominated by lengthy textual content like research journal articles.
A Survey of Large Language Models in Finance (FinLLMs)
This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges.
SCO-VIST: Social Interaction Commonsense Knowledge-based Visual Storytelling
This weighted story graph produces the storyline in a sequence of events using Floyd-Warshall's algorithm.
Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion
Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems.
MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction
Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives.
Workshop on Document Intelligence Understanding
Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically.
Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets
We present a novel tri-level joint natural language understanding approach, adding domain, and explicitly exchange semantic information between all levels.
Graph Neural Networks for Text Classification: A Survey
Text Classification is the most essential and fundamental problem in Natural Language Processing.
PDFVQA: A New Dataset for Real-World VQA on PDF Documents
Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions.
Form-NLU: Dataset for the Form Natural Language Understanding
Compared to general document analysis tasks, form document structure understanding and retrieval are challenging.
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series
There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited.
Emotion Classification
Multivariate Time Series Forecasting
+2
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction
This tutorial will discuss how the joint task is set up and introduce Spoken Language Understanding/Natural Language Understanding (SLU/NLU) with Deep Learning techniques.
PiggyBack: Pretrained Visual Question Answering Environment for Backing up Non-deep Learning Professionals
We propose a PiggyBack, a Visual Question Answering platform that allows users to apply the state-of-the-art visual-language pretrained models easily.
In-game Toxic Language Detection: Shared Task and Attention Residuals
In-game toxic language becomes the hot potato in the gaming industry and community.
SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation
Scene Graph Generation (SGG) serves a comprehensive representation of the images for human understanding as well as visual understanding tasks.
SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation
Collaborative filtering problems are commonly solved based on matrix completion techniques which recover the missing values of user-item interaction matrices.
K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment
Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited.
Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis
Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications.
Understanding Attention for Vision-and-Language Tasks
Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features.
A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning
For preventing youth suicide, social media platforms have received much attention from researchers.
InducT-GCN: Inductive Graph Convolutional Networks for Text Classification
Under the extreme settings with no extra resource and limited amount of training set, can we still learn an inductive graph-based text classification model?
V-Doc : Visual questions answers with Documents
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks.
Vision-and-Language Pretrained Models: A Survey
Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP).
ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification
Our ME-GCN can integrate a rich source of graph edge information of the entire text corpus.
Understanding Graph Convolutional Networks for Text Classification
Graph Convolutional Networks (GCN) have been effective at tasks that have rich relational structure and can preserve global structure information of a dataset in graph embeddings.
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling
In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling.
V-Doc: Visual Questions Answers With Documents
We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks.
GLocal-K: Global and Local Kernels for Recommender Systems
Then, the pre-trained auto encoder is fine-tuned with the rating matrix, produced by a convolution-based global kernel, which captures the characteristics of each item.
Ranked #1 on
Recommendation Systems
on Douban Monti
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Accompanying the dataset is a thorough in-game toxicity analysis, which provides comprehensive understanding of context at utterance, token, and dual levels.
FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
The Federal Reserve System (the Fed) plays a significant role in affecting monetary policy and financial conditions worldwide.
A Survey on Extraction of Causal Relations from Natural Language Text
Existing causality extraction techniques include knowledge-based, statistical machine learning(ML)-based, and deep learning-based approaches.
Detect All Abuse! Toward Universal Abusive Language Detection Models
Online abusive language detection (ALD) has become a societal issue of increasing importance in recent years.
VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks
We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input.
Ranked #24 on
Text-to-Image Generation
on MS COCO
(Inception score metric)
REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering
Visual question answering (VQA) is a challenging multi-modal task that requires not only the semantic understanding of both images and questions, but also the sound perception of a step-by-step reasoning process that would lead to the correct answer.
