Tootfinder

BuDDIE: A Business Document Dataset for Multi-task Information Extraction
Ran Zmigrod, Dongsheng Wang, Mathieu Sibue, Yulong Pei, Petr Babkin, Ivan Brugere, Xiaomo Liu, Nacho Navarro, Antony Papadimitriou, William Watson, Zhiqiang Ma, Armineh Nourbakhsh, Sameena Shah
https://arxiv.org/abs/2404.04003

BuDDIE: A Business Document Dataset for Multi-task Information Extraction
The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in a multi-modal domain. Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia. These datasets cover documents like invoices and receipts with sparse annotations such that they support one or two co-related tasks (e.g., entity extraction and entity lin…

Multi-hop Question Answering over Knowledge Graphs using Large Language Models
Abir Chakraborty
https://arxiv.org/abs/2404.19234 https://

Multi-hop Question Answering over Knowledge Graphs using Large Language Models
Knowledge graphs (KGs) are large datasets with specific structures representing large knowledge bases (KB) where each node represents a key entity and relations amongst them are typed edges. Natural language queries formed to extract information from a KB entail starting from specific nodes and reasoning over multiple edges of the corresponding KG to arrive at the correct set of answer nodes. Traditional approaches of question answering on KG are based on (a) semantic parsing (SP), where a logi…

2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion
Dongsheng Wang, Xiaoqin Feng, Zeming Liu, Chuan Wang
https://arxiv.org/abs/2404.17122

2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion
Named entity recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities in sentences into pre-defined types. It plays a crucial role in various research fields, including entity linking, question answering, and online product recommendation. Recent studies have shown that incorporating multilingual and multimodal datasets can enhance the effectiveness of NER. This is due to language transfer learning and the presence of shared impli…

This https://arxiv.org/abs/2310.12491 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Hiding Access-pattern is Not Enough! Veil: A Storage and Communication Efficient Volume-Hiding Algorithm
This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent volume leakage, entitled Veil, that partitions the dataset by randomly mapping keys to a set of equi-sized buckets. Veil provides a tunable me…

This https://arxiv.org/abs/2310.12491 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Hiding Access-pattern is Not Enough! Veil: A Storage and Communication Efficient Volume-Hiding Algorithm
This paper addresses volume leakage (i.e., leakage of the number of records in the answer set) when processing keyword queries in encrypted key-value (KV) datasets. Volume leakage, coupled with prior knowledge about data distribution and/or previously executed queries, can reveal both ciphertexts and current user queries. We develop a solution to prevent volume leakage, entitled Veil, that partitions the dataset by randomly mapping keys to a set of equi-sized buckets. Veil provides a tunable me…