Distilling the Knowledge of Romanian BERTs Using Multiple Teachers

• URL: http://arxiv.org/abs/2112.12650v1
• Date: Thu, 23 Dec 2021 15:37:58 GMT
• Title: Distilling the Knowledge of Romanian BERTs Using Multiple Teachers
• Authors: Andrei-Marius Avram, Darius Catrina, Dumitru-Clementin Cercel, Mihai Dasc\u{a}lu, Traian Rebedea, Vasile P\u{a}i\c{s}, Dan Tufi\c{s}
• Abstract summary: We introduce three light and fast versions of distilled BERT models for the Romanian language. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models.
• Score: 0.47169950535905025
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As transfer learning from large-scale pre-trained language models has become prevalent in Natural Language Processing, running these models in computationally constrained environments remains a challenging problem yet to address. Several solutions including knowledge distillation, network quantization or network pruning have been proposed; however, these approaches focus mostly on the English language, thus widening the gap when considering low-resource languages. In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base and DistilMulti-BERT-base-ro. The first two models resulted from individually distilling the knowledge of the two base versions of Romanian BERTs available in literature, while the last one was obtained by distilling their ensemble. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models, which were thoroughly evaluated on five tasks: part-of-speech tagging, named entity recognition, sentiment analysis, semantic textual similarity and dialect identification. The experimental results on these benchmarks proved that our three distilled models maintain most performance in terms of accuracy with their teachers, while being twice as fast on a GPU and ~35\% smaller. In addition, we further test the similarity between our students and their teachers prediction by measuring their label and probability loyalty, together with regression loyalty - a new metric introduced in this work.

Related papers

• 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)
• BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer [81.5984433881309]
  We introduce BUFFET, which unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format. BUFFET is designed to establish a rigorous and equitable evaluation framework for few-shot cross-lingual transfer. Our findings reveal significant room for improvement in few-shot in-context cross-lingual transfer.
  arXiv  Detail & Related papers  (2023-05-24T08:06:33Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese [4.4681678689625715]
  We analyse the effect of pre-training with monolingual data for a low-resource language. We present a newly created corpus for Maltese, and determine the effect that the pre-training data size and domain have on the downstream performance. We compare two models on the new corpus: a monolingual BERT model trained from scratch (BERTu), and a further pre-trained multilingual BERT (mBERTu)
  arXiv  Detail & Related papers  (2022-05-21T06:44:59Z)
• IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages [87.5457337866383]
  We introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. We find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks.
  arXiv  Detail & Related papers  (2022-01-27T18:53:22Z)
• DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing [117.41016786835452]
  This paper presents a new pre-trained language model, DeBERTaV3, which improves the original DeBERTa model. vanilla embedding sharing in ELECTRA hurts training efficiency and model performance. We propose a new gradient-disentangled embedding sharing method that avoids the tug-of-war dynamics.
  arXiv  Detail & Related papers  (2021-11-18T06:48:00Z)
• Interpreting Language Models Through Knowledge Graph Extraction [42.97929497661778]
  We compare BERT-based language models through snapshots of acquired knowledge at sequential stages of the training process. We present a methodology to unveil a knowledge acquisition timeline by generating knowledge graph extracts from cloze "fill-in-the-blank" statements. We extend this analysis to a comparison of pretrained variations of BERT models (DistilBERT, BERT-base, RoBERTa)
  arXiv  Detail & Related papers  (2021-11-16T15:18:01Z)
• Learning Compact Metrics for MT [21.408684470261342]
  We investigate the trade-off between multilinguality and model capacity with RemBERT, a state-of-the-art multilingual language model. We show that model size is indeed a bottleneck for cross-lingual transfer, then demonstrate how distillation can help addressing this bottleneck. Our method yields up to 10.5% improvement over vanilla fine-tuning and reaches 92.6% of RemBERT's performance using only a third of its parameters.
  arXiv  Detail & Related papers  (2021-10-12T20:39:35Z)
• Mono vs Multilingual Transformer-based Models: a Comparison across Several Language Tasks [1.2691047660244335]
  BERT (Bidirectional Representations from Transformers) and ALBERT (A Lite BERT) are methods for pre-training language models. We make available our trained BERT and Albert model for Portuguese.
  arXiv  Detail & Related papers  (2020-07-19T19:13:20Z)
• Cross-lingual Information Retrieval with BERT [8.052497255948046]
  We explore the use of the popular bidirectional language model, BERT, to model and learn the relevance between English queries and foreign-language documents. A deep relevance matching model based on BERT is introduced and trained by finetuning a pretrained multilingual BERT model with weak supervision. Experimental results of the retrieval of Lithuanian documents against short English queries show that our model is effective and outperforms the competitive baseline approaches.
  arXiv  Detail & Related papers  (2020-04-24T23:32:13Z)

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.