The Classical Language Toolkit: An NLP Framework for Pre-Modern Languages (2021.acl-demo)
Copied to clipboard
Kyle P. Johnson, Patrick J. Burns, John Stewart, Todd Cook, Clément Besnier, William J. B. Mattingly
| Challenge: | Classical Language Toolkit (CLTK) is an NLP framework for pre-modern languages . authors say it assumes pre-existing living languages, neglecting important characteristics of non-spoken historical languages despite their existence . |
| Approach: | The paper announces version 1.0 of the Classical Language Toolkit (CLTK) it is an NLP framework for pre-modern languages that uses assumptions specific to living languages . authors propose a modular processing pipeline that balances competing demands of algorithmic diversity with pre-configured defaults . |
| Outcome: | The Classical Language Toolkit (CLTK) is a new NLP framework for pre-modern languages . the framework is based on the existing frameworks and is available for almost 20 languages - including models . |
Similar Papers
GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek (2025.coling-demos)
Copied to clipboard
Lefteris Loukas, Nikolaos Smyrnioudis, Chrysa Dikonomaki, Spiros Barbakos, Anastasios Toumazatos, John Koutsikakis, Manolis Kyriakakis, Mary Georgiou, Stavros Vassos, John Pavlopoulos, Ion Androutsopoulos
| Challenge: | GR-NLP-TOOLKIT is an open-source natural language processing toolkit for modern Greek. |
| Approach: | They present GR-NLP-TOOLKIT, an open-source natural language processing toolkit for Greek. |
| Outcome: | The toolkit provides state-of-the-art performance in five core NLP tasks . it can be easily installed in Python and is accessible through a demonstration platform on HuggingFace . |
CogCompNLP: Your Swiss Army Knife for NLP (L18-1)
Copied to clipboard
Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
| Challenge: | a corpus-reader module supports popular corpora, feature extraction and annotation modules for semantic and syntactic tasks. |
| Approach: | They propose a library that provides modules to address different challenges . they provide a corpus-reader module that supports popular corpora in the NLP community . |
| Outcome: | The proposed library simplifies the process of design and development of NLP applications by providing modules to address different challenges. |
Exploring Large Language Models for Classical Philology (2023.acl-long)
Copied to clipboard
| Challenge: | Recent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. |
| Approach: | They propose to use encoder-only and encoder decoder architectures to create four models for Ancient Greek that vary along two dimensions for tasks of interest for Classical languages. |
| Outcome: | The proposed models improve on existing models of Ancient Greek and Latin and provide a large pre-training corpus for Ancient Greek to support the creation of a larger, comparable model zoo for Classical Philology. |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
Copied to clipboard
Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |
First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | a new system trained on well over a trillion words smashes the state of the art by a margin previously thought impossible. |
| Approach: | They argue that disparities in scale are transient and researchers can work to reduce them . they argue that data, rather than hardware, is still a bottleneck for many applications . |
| Outcome: | a new system trained on well over a trillion words smashes the state of the art by a margin previously thought impossible. |
Systematic Inequalities in Language Technology Performance across the World’s Languages (2022.acl-long)
Copied to clipboard
| Challenge: | Recent studies have revealed that NLP is limited to a subset of the world’s 6,500 languages. |
| Approach: | They propose a framework for estimating the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
| Outcome: | The proposed framework estimates the global utility of language technologies as revealed in a comprehensive snapshot of recent publications in NLP. |
Defining a New NLP Playground (2023.findings-emnlp)
Copied to clipboard
Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, Heng Ji
| Challenge: | Recent explosion of performance of large language models (LLMs) has changed the field more abruptly and seismically than any other shift in the field’s 80 year history. |
| Approach: | They propose 20+ PhD-dissertation-worthy research directions to define a new NLP playground by combining theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
| Outcome: | The proposed research will cover theoretical analysis, new and challenging problems, learning paradigms and interdisciplinary applications. |
NLPre: A Revised Approach towards Language-centric Benchmarking of Natural Language Preprocessing Systems (2024.lrec-main)
Copied to clipboard
| Challenge: | GLUE benchmarking system enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance. |
| Approach: | They propose a language-centric benchmarking system that enables ongoing evaluation of multiple NLPre tools while credibly tracking their performance. |
| Outcome: | The proposed system is configured for Polish and integrated with the thoroughly assembled NLPre-PL benchmark. |
Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis (2022.findings-naacl)
Copied to clipboard
| Challenge: | PPCEME has a large set of function tags and is difficult to parse . authors present results for PPceME using a modified version of the Berkeley Neural Parser . |
| Approach: | They propose to use a modified version of the Berkeley Neural Parser to parse PPCEME using function tags. |
| Outcome: | The proposed parser will be used to parse Early English Books Online, a 1.5 billion word corpus. |
Synthetic Data in the Era of Large Language Models (2025.acl-tutorials)
Copied to clipboard
| Challenge: | 'synthetic data' is a data generated with the assistance of large language models to make dataset construction faster and cheaper. |
| Approach: | This tutorial seeks to build a shared understanding of recent progress in synthetic data generation from NLP and related fields by grouping and describing major methods, applications, and open problems. |
| Outcome: | This tutorial will describe methods, applications, and open problems that have been developed and are being used to improve the quality and efficiency of synthetic data generation. |