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Found 15 papers, 7 papers with code
On the Consideration of AI Openness: Can Good Intent Be Abused?
Openness is critical for the advancement of science.
SymBa: Symbolic Backward Chaining for Multi-step Natural Language Reasoning
Large Language Models (LLMs) have recently demonstrated remarkable reasoning ability as in Chain-of-thought prompting, but faithful multi-step reasoning remains a challenge.
NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus
Here we provide NESTLE, a no-code tool for large-scale statistical analysis of legal corpus.
Data-efficient End-to-end Information Extraction for Statistical Legal Analysis
Lawyers, for instance, search for appropriate precedents favorable to their clients, while the number of legal precedents is ever-growing.
A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction
Here we present the first large-scale benchmark of Korean legal AI datasets, LBOX OPEN, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task.
Semi-Structured Query Grounding for Document-Oriented Databases with Deep Retrieval and Its Application to Receipt and POI Matching
Semi-structured query systems for document-oriented databases have many real applications.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
OCR-free Document Understanding Transformer
Current Visual Document Understanding (VDU) methods outsource the task of reading text to off-the-shelf Optical Character Recognition (OCR) engines and focus on the understanding task with the OCR outputs.
Ranked #10 on
Document Image Classification
on RVL-CDIP
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents
On the other hand, this paper tackles the problem by going back to the basic: effective combination of text and layout.
Ranked #5 on
Relation Extraction
on FUNSD
Cost-effective End-to-end Information Extraction for Semi-structured Document Images
A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost.
BROS: A Pre-trained Language Model for Understanding Texts in Document
Although the recent advance in OCR enables the accurate extraction of text segments, it is still challenging to extract key information from documents due to the diversity of layouts.
Tractable loss function and color image generation of multinary restricted Boltzmann machine
The restricted Boltzmann machine (RBM) is a representative generative model based on the concept of statistical mechanics.
Syntactic Question Abstraction and Retrieval for Data-Scarce Semantic Parsing
Deep learning approaches to semantic parsing require a large amount of labeled data, but annotating complex logical forms is costly.
Spatial Dependency Parsing for Semi-Structured Document Information Extraction
Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories.
Post-OCR parsing: building simple and robust parser via BIO tagging
Parsing textual information embedded in images is important for various down- stream tasks.
Optical Character Recognition
Optical Character Recognition (OCR)
A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization
We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.
