Playing with Words at the National Library of Sweden -- Making a Swedish BERT

• URL: http://arxiv.org/abs/2007.01658v1
• Date: Fri, 3 Jul 2020 12:53:39 GMT
• Title: Playing with Words at the National Library of Sweden -- Making a Swedish BERT
• Authors: Martin Malmsten, Love B\"orjeson and Chris Haffenden
• Abstract summary: This paper introduces the Swedish BERT ("KB-BERT") developed by the KBLab for data-driven research at the National Library of Sweden (KB) Building on recent efforts to create transformer-based BERT models for languages other than English, we explain how we used KB's collections to create and train a new language-specific BERT model for Swedish.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces the Swedish BERT ("KB-BERT") developed by the KBLab for data-driven research at the National Library of Sweden (KB). Building on recent efforts to create transformer-based BERT models for languages other than English, we explain how we used KB's collections to create and train a new language-specific BERT model for Swedish. We also present the results of our model in comparison with existing models - chiefly that produced by the Swedish Public Employment Service, Arbetsf\"ormedlingen, and Google's multilingual M-BERT - where we demonstrate that KB-BERT outperforms these in a range of NLP tasks from named entity recognition (NER) to part-of-speech tagging (POS). Our discussion highlights the difficulties that continue to exist given the lack of training data and testbeds for smaller languages like Swedish. We release our model for further exploration and research here: https://github.com/Kungbib/swedish-bert-models .

Related papers

• SWEb: A Large Web Dataset for the Scandinavian Languages [11.41086713693524]
  This paper presents the largest pretraining dataset for the Scandinavian languages: the Scandinavian WEb (SWEb) We introduce a novel model-based text extractor that significantly reduces complexity in comparison with rule-based approaches. We also introduce a new cloze-style benchmark for evaluating language models in Swedish, and use this test to compare models trained on the SWEb data to models trained on FineWeb, with competitive results.
  arXiv  Detail & Related papers  (2024-10-06T11:55:15Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages [0.0]
  We train a trilingual LitLat BERT-like model for Lithuanian, Latvian, and English, and a monolingual Est-RoBERTa model for Estonian. We evaluate their performance on four downstream tasks: named entity recognition, dependency parsing, part-of-speech tagging, and word analogy.
  arXiv  Detail & Related papers  (2021-12-20T14:26:40Z)
• FBERT: A Neural Transformer for Identifying Offensive Content [67.12838911384024]
  fBERT is a BERT model retrained on SOLID, the largest English offensive language identification corpus available with over $1.4$ million offensive instances. We evaluate fBERT's performance on identifying offensive content on multiple English datasets and we test several thresholds for selecting instances from SOLID. The fBERT model will be made freely available to the community.
  arXiv  Detail & Related papers  (2021-09-10T19:19:26Z)
• Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model [0.0]
  We show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks.
  arXiv  Detail & Related papers  (2021-04-19T20:36:24Z)
• EstBERT: A Pretrained Language-Specific BERT for Estonian [0.3674863913115431]
  This paper presents EstBERT, a large pretrained transformer-based language-specific BERT model for Estonian. Recent work has evaluated multilingual BERT models on Estonian tasks and found them to outperform the baselines. We show that the models based on EstBERT outperform multilingual BERT models on five tasks out of six.
  arXiv  Detail & Related papers  (2020-11-09T21:33:53Z)
• Learning Contextualised Cross-lingual Word Embeddings and Alignments for Extremely Low-Resource Languages Using Parallel Corpora [63.5286019659504]
  We propose a new approach for learning contextualised cross-lingual word embeddings based on a small parallel corpus. Our method obtains word embeddings via an LSTM encoder-decoder model that simultaneously translates and reconstructs an input sentence.
  arXiv  Detail & Related papers  (2020-10-27T22:24:01Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data [113.29476656550342]
  We present TaBERT, a pretrained LM that jointly learns representations for NL sentences and tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. Implementation of the model will be available at http://fburl.com/TaBERT.
  arXiv  Detail & Related papers  (2020-05-17T17:26:40Z)
• CodeBERT: A Pre-Trained Model for Programming and Natural Languages [117.34242908773061]
  CodeBERT is a pre-trained model for programming language (PL) and nat-ural language (NL) We develop CodeBERT with Transformer-based neural architecture. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters.
  arXiv  Detail & Related papers  (2020-02-19T13:09:07Z)
• RobBERT: a Dutch RoBERTa-based Language Model [9.797319790710711]
  We use RoBERTa to train a Dutch language model called RobBERT. We measure its performance on various tasks as well as the importance of the fine-tuning dataset size. RobBERT improves state-of-the-art results for various tasks, and especially significantly outperforms other models when dealing with smaller datasets.
  arXiv  Detail & Related papers  (2020-01-17T13:25:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.