Search Results for author:
Found 11 papers, 5 papers with code
Challenging America: Modeling language in longer time scales
The aim of the paper is to apply, for historical texts, the methodology used commonly to solve various NLP tasks defined for contemporary data, i. e. pre-train and fine-tune large Transformer models.
CCpdf: Building a High Quality Corpus for Visually Rich Documents from Web Crawl Data
In recent years, the field of document understanding has progressed a lot.
Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts
The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems.
From Dataset Recycling to Multi-Property Extraction and Beyond
This paper investigates various Transformer architectures on the WikiReading Information Extraction and Machine Reading Comprehension dataset.
Dynamic Boundary Time Warping for Sub-sequence Matching with Few Examples
The paper presents a novel method of finding a fragment in a long temporal sequence similar to the set of shorter sequences.
Ranked #2 on
Semantic Retrieval
on Contract Discovery
Successive Halving Top-k Operator
We propose a differentiable successive halving method of relaxing the top-k operator, rendering gradient-based optimization possible.
On the Multi-Property Extraction and Beyond
In this paper, we investigate the Dual-source Transformer architecture on the WikiReading information extraction and machine reading comprehension dataset.
ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them
This paper presents the winning system for the propaganda Technique Classification (TC) task and the second-placed system for the propaganda Span Identification (SI) task.
Kleister: A novel task for Information Extraction involving Long Documents with Complex Layout
State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a broad range of contexts, like the sentence-level context or document-level context for short documents.
LAMBERT: Layout-Aware (Language) Modeling for information extraction
We introduce a simple new approach to the problem of understanding documents where non-trivial layout influences the local semantics.
Ranked #3 on
Key Information Extraction
on Kleister NDA
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines
We propose a new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed, where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts.
Ranked #1 on
Semantic Retrieval
on Contract Discovery
