Search Results for author:
Found 9 papers, 2 papers with code
GPT-SW3: An Autoregressive Language Model for the Nordic Languages
This paper details the process of developing the first native large generative language model for the Nordic languages, GPT-SW3.
The Nordic Pile: A 1.2TB Nordic Dataset for Language Modeling
Pre-training Large Language Models (LLMs) require massive amounts of text data, and the performance of the LLMs typically correlates with the scale and quality of the datasets.
Cross-lingual Transfer of Monolingual Models
Additionally, the results of evaluating the transferred models in source language tasks reveal that their performance in the source domain deteriorates after transfer.
Should we Stop Training More Monolingual Models, and Simply Use Machine Translation Instead?
We demonstrate empirically that a large English language model coupled with modern machine translation outperforms native language models in most Scandinavian languages.
Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity
This paper presents the first Swedish evaluation benchmark for textual semantic similarity.
Automatic Extraction of Personality from Text: Challenges and Opportunities
Our results show that the models based on the small high-reliability dataset performed better (in terms of $\textrm{R}^2$) than models based on large low-reliability dataset.
Dick-Preston and Morbo at SemEval-2019 Task 4: Transfer Learning for Hyperpartisan News Detection
In a world of information operations, influence campaigns, and fake news, classification of news articles as following hyperpartisan argumentation or not is becoming increasingly important.
Learning Representations for Detecting Abusive Language
This paper discusses the question whether it is possible to learn a generic representation that is useful for detecting various types of abusive language.
Monitoring Targeted Hate in Online Environments
Hateful comments, swearwords and sometimes even death threats are becoming a reality for many people today in online environments.
