20 citations · 24 across the 6 of their papers we have counts for
9 papers · 1 filter
Small Languages, Big Models: A Study of Continual Training on Languages of Norway
David Samuel, Vladislav Mikhailov, Erik Velldal +4
Training large language models requires vast amounts of data, posing a challenge for less widely spoken languages like Norwegian and even more so for truly low-resource languages l…
Entity-Level Sentiment: More than the Sum of Its Parts
Egil Rønningstad, Roman Klinger, Lilja Øvrelid +1
In sentiment analysis of longer texts, there may be a variety of topics discussed, of entities mentioned, and of sentiments expressed regarding each entity. We find a lack of studi…
Compositional Generalization with Grounded Language Models
Sondre Wold, Étienne Simon, Lucas Georges Gabriel Charpentier +3
Grounded language models use external sources of information, such as knowledge graphs, to meet some of the general challenges associated with pre-training. By extending previous w…
Text-To-KG Alignment: Comparing Current Methods on Classification Tasks
Sondre Wold, Lilja Øvrelid, Erik Velldal
In contrast to large text corpora, knowledge graphs (KG) provide dense and structured representations of factual information. This makes them attractive for systems that supplement…
NorBench -- A Benchmark for Norwegian Language Models
David Samuel, Andrey Kutuzov, Samia Touileb +5
We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduc…
Entity-Level Sentiment Analysis (ELSA): An exploratory task survey
Egil Rønningstad, Erik Velldal, Lilja Øvrelid
This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document -- what we refer to as Entity-Leve…