Ariel Ekgren


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Cross-lingual Transfer of Monolingual Models
Evangelia Gogoulou | Ariel Ekgren | Tim Isbister | Magnus Sahlgren
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Recent studies in cross-lingual learning using multilingual models have cast doubt on the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization. We introduce a method for transferring monolingual models to other languages through continuous pre-training and study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform an English model trained from scratch, independently of the source language. After probing the model representations, we find that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in English.

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Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish
Ariel Ekgren | Amaru Cuba Gyllensten | Evangelia Gogoulou | Alice Heiman | Severine Verlinden | Joey Öhman | Fredrik Carlsson | Magnus Sahlgren
Proceedings of the Thirteenth Language Resources and Evaluation Conference

We present GTP-SW3, a 3.5 billion parameter autoregressive language model, trained on a newly created 100 GB Swedish corpus. This paper provides insights with regards to data collection and training, while highlights the challenges of proper model evaluation. The results of quantitive evaluation through perplexity indicate that GPT-SW3 is a competent model in comparison with existing autoregressive models of similar size. Additionally, we perform an extensive prompting study which reveals the good text generation capabilities of GTP-SW3.


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SenseCluster at SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
Amaru Cuba Gyllensten | Evangelia Gogoulou | Ariel Ekgren | Magnus Sahlgren
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We (Team Skurt) propose a simple method to detect lexical semantic change by clustering contextualized embeddings produced by XLM-R, using K-Means++. The basic idea is that contextualized embeddings that encode the same sense are located in close proximity in the embedding space. Our approach is both simple and generic, but yet performs relatively good in both sub-tasks of SemEval-2020 Task 1. We hypothesize that the main shortcoming of our method lies in the simplicity of the clustering method used.

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Text Categorization for Conflict Event Annotation
Fredrik Olsson | Magnus Sahlgren | Fehmi ben Abdesslem | Ariel Ekgren | Kristine Eck
Proceedings of the Workshop on Automated Extraction of Socio-political Events from News 2020

We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g., who were the attacking organization, who was attacked, and where did the event take place. The text categorization techniques under scrutiny are a classical Bag-of-Words approach; character-based contextualized embeddings produced by ELMo; embeddings produced by the BERT base model, and a version of BERT base fine-tuned on UCDP data; and a pre-trained and fine-tuned classifier based on ULMFiT. The categorization tasks are very diverse in terms of the number of classes to predict as well as the skeweness of the distribution of classes. The categorization results exhibit a large variability across tasks, ranging from 30.3% to 99.8% F-score.


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R-grams: Unsupervised Learning of Semantic Units in Natural Language
Amaru Cuba Gyllensten | Ariel Ekgren | Magnus Sahlgren
Proceedings of the 13th International Conference on Computational Semantics - Student Papers

This paper investigates data-driven segmentation using Re-Pair or Byte Pair Encoding-techniques. In contrast to previous work which has primarily been focused on subword units for machine translation, we are interested in the general properties of such segments above the word level. We call these segments r-grams, and discuss their properties and the effect they have on the token frequency distribution. The proposed approach is evaluated by demonstrating its viability in embedding techniques, both in monolingual and multilingual test settings. We also provide a number of qualitative examples of the proposed methodology, demonstrating its viability as a language-invariant segmentation procedure.