activity
20162024
most citedAttending to Characters in Neural Sequence Labeling Models

67 citations · 76 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20241 cited

A Survey of Large Language Models for European Languages

Wazir Ali, Sampo Pyysalo

Large Language Models (LLMs) have gained significant attention due to their high performance on a wide range of natural language tasks since the release of ChatGPT. The LLMs learn…

cs.CL20246 cited

A New Massive Multilingual Dataset for High-Performance Language Technologies

Ona de Gibert, Graeme Nail, Nikolay Arefyev +10

We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from…

cs.CL20231 cited

FinGPT: Large Generative Models for a Small Language

Risto Luukkonen, Ville Komulainen, Jouni Luoma +18

Large language models (LLMs) excel in many tasks in NLP and beyond, but most open models have very limited coverage of smaller languages and LLM work tends to focus on languages wh…

cs.CL2023

Silver Syntax Pre-training for Cross-Domain Relation Extraction

Elisa Bassignana, Filip Ginter, Sampo Pyysalo +2

Relation Extraction (RE) remains a challenging task, especially when considering realistic out-of-domain evaluations. One of the main reasons for this is the limited training size…

cs.CL20231 cited

Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction

Elisa Bassignana, Filip Ginter, Sampo Pyysalo +2

Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE, the broadest multi-lingual dat…

cs.CL201667 cited

Attending to Characters in Neural Sequence Labeling Models

Marek Rei, Gamal K. O. Crichton, Sampo Pyysalo

Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to…