RobBERT: a Dutch RoBERTa-based Language Model

• URL: http://arxiv.org/abs/2001.06286v2
• Date: Wed, 16 Sep 2020 13:42:16 GMT
• Title: RobBERT: a Dutch RoBERTa-based Language Model
• Authors: Pieter Delobelle, Thomas Winters, Bettina Berendt
• Abstract summary: 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.
• Score: 9.797319790710711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained language models have been dominating the field of natural language processing in recent years, and have led to significant performance gains for various complex natural language tasks. One of the most prominent pre-trained language models is BERT, which was released as an English as well as a multilingual version. Although multilingual BERT performs well on many tasks, recent studies show that BERT models trained on a single language significantly outperform the multilingual version. Training a Dutch BERT model thus has a lot of potential for a wide range of Dutch NLP tasks. While previous approaches have used earlier implementations of BERT to train a Dutch version of BERT, we used RoBERTa, a robustly optimized BERT approach, to train a Dutch language model called RobBERT. We measured its performance on various tasks as well as the importance of the fine-tuning dataset size. We also evaluated the importance of language-specific tokenizers and the model's fairness. We found that RobBERT improves state-of-the-art results for various tasks, and especially significantly outperforms other models when dealing with smaller datasets. These results indicate that it is a powerful pre-trained model for a large variety of Dutch language tasks. The pre-trained and fine-tuned models are publicly available to support further downstream Dutch NLP applications.

Related papers

• 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)
• Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey [67.82942975834924]
  Large, pre-trained language models such as BERT have drastically changed the Natural Language Processing (NLP) field. We present a survey of recent work that uses these large language models to solve NLP tasks via pre-training then fine-tuning, prompting, or text generation approaches.
  arXiv  Detail & Related papers  (2021-11-01T20:08:05Z)
• bert2BERT: Towards Reusable Pretrained Language Models [51.078081486422896]
  We propose bert2BERT, which can effectively transfer the knowledge of an existing smaller pre-trained model to a large model. bert2BERT saves about 45% and 47% computational cost of pre-training BERT_BASE and GPT_BASE by reusing the models of almost their half sizes.
  arXiv  Detail & Related papers  (2021-10-14T04:05:25Z)
• UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
  Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages. We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
  arXiv  Detail & Related papers  (2020-12-31T11:37:28Z)
• 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)
• Evaluating Multilingual BERT for Estonian [0.8057006406834467]
  We evaluate four multilingual models -- multilingual BERT, multilingual distilled BERT, XLM and XLM-RoBERTa -- on several NLP tasks. Our results show that multilingual BERT models can generalise well on different Estonian NLP tasks.
  arXiv  Detail & Related papers  (2020-10-01T14:48:31Z)
• Multilingual Translation with Extensible Multilingual Pretraining and Finetuning [77.33262578776291]
  Previous work has demonstrated that machine translation systems can be created by finetuning on bitext. We show that multilingual translation models can be created through multilingual finetuning. We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance.
  arXiv  Detail & Related papers  (2020-08-02T05:36:55Z)
• WikiBERT models: deep transfer learning for many languages [1.3455090151301572]
  We introduce a simple, fully automated pipeline for creating languagespecific BERT models from Wikipedia data. We assess the merits of these models using the state-of-the-art UDify on Universal Dependencies data.
  arXiv  Detail & Related papers  (2020-06-02T11:57:53Z)
• ParsBERT: Transformer-based Model for Persian Language Understanding [0.7646713951724012]
  This paper proposes a monolingual BERT for the Persian language (ParsBERT) It shows its state-of-the-art performance compared to other architectures and multilingual models. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones.
  arXiv  Detail & Related papers  (2020-05-26T05:05:32Z)
• Revisiting Pre-Trained Models for Chinese Natural Language Processing [73.65780892128389]
  We revisit Chinese pre-trained language models to examine their effectiveness in a non-English language. We also propose a model called MacBERT, which improves upon RoBERTa in several ways.
  arXiv  Detail & Related papers  (2020-04-29T02:08:30Z)
• A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT [60.9051207862378]
  multilingual BERT works remarkably well on cross-lingual transfer tasks. Datasize and context window size are crucial factors to the transferability. There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
  arXiv  Detail & Related papers  (2020-04-20T11:13:16Z)

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.