PERT: Pre-training BERT with Permuted Language Model

• URL: http://arxiv.org/abs/2203.06906v1
• Date: Mon, 14 Mar 2022 07:58:34 GMT
• Title: PERT: Pre-training BERT with Permuted Language Model
• Authors: Yiming Cui, Ziqing Yang, Ting Liu
• Abstract summary: PERT is an auto-encoding model (like BERT) trained with Permuted Language Model (PerLM) We permute a proportion of the input text, and the training objective is to predict the position of the original token. We carried out extensive experiments on both Chinese and English NLU benchmarks.
• Score: 24.92527883997854
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pre-trained Language Models (PLMs) have been widely used in various natural language processing (NLP) tasks, owing to their powerful text representations trained on large-scale corpora. In this paper, we propose a new PLM called PERT for natural language understanding (NLU). PERT is an auto-encoding model (like BERT) trained with Permuted Language Model (PerLM). The formulation of the proposed PerLM is straightforward. We permute a proportion of the input text, and the training objective is to predict the position of the original token. Moreover, we also apply whole word masking and N-gram masking to improve the performance of PERT. We carried out extensive experiments on both Chinese and English NLU benchmarks. The experimental results show that PERT can bring improvements over various comparable baselines on some of the tasks, while others are not. These results indicate that developing more diverse pre-training tasks is possible instead of masked language model variants. Several quantitative studies are carried out to better understand PERT, which might help design PLMs in the future. Resources are available: https://github.com/ymcui/PERT

Related papers

• Few-Shot Cross-Lingual Transfer for Prompting Large Language Models in Low-Resource Languages [0.0]
  "prompting" is where a user provides a description of a task and some completed examples of the task to a PLM as context before prompting the PLM to perform the task on a new example. We consider three methods: few-shot prompting (prompt), language-adaptive fine-tuning (LAFT), and neural machine translation (translate) We find that translate and prompt settings are a compute-efficient and cost-effective method of few-shot prompting for the selected low-resource languages.
  arXiv  Detail & Related papers  (2024-03-09T21:36:13Z)
• Improving Language Plasticity via Pretraining with Active Forgetting [63.36484652568976]
  We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Experiments with RoBERTa show that models pretrained with our forgetting mechanism demonstrate faster convergence during language adaptation.
  arXiv  Detail & Related papers  (2023-07-03T17:12:44Z)
• Comparison of Pre-trained Language Models for Turkish Address Parsing [0.0]
  We focus on Turkish maps data and thoroughly evaluate both multilingual and Turkish based BERT, DistilBERT, ELECTRA and RoBERTa. We also propose a MultiLayer Perceptron (MLP) for fine-tuning BERT in addition to the standard approach of one-layer fine-tuning.
  arXiv  Detail & Related papers  (2023-06-24T12:09:43Z)
• LERT: A Linguistically-motivated Pre-trained Language Model [67.65651497173998]
  We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original pre-training task. We carried out extensive experiments on ten Chinese NLU tasks, and the experimental results show that LERT could bring significant improvements.
  arXiv  Detail & Related papers  (2022-11-10T05:09:16Z)
• Prompt Tuning for Discriminative Pre-trained Language Models [96.04765512463415]
  Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks. It is still unknown whether and how discriminative PLMs, e.g., ELECTRA, can be effectively prompt-tuned. We present DPT, the first prompt tuning framework for discriminative PLMs, which reformulates NLP tasks into a discriminative language modeling problem.
  arXiv  Detail & Related papers  (2022-05-23T10:11:50Z)
• LaoPLM: Pre-trained Language Models for Lao [3.2146309563776416]
  Pre-trained language models (PLMs) can capture different levels of concepts in context and hence generate universal language representations. Although PTMs have been widely used in most NLP applications, it is under-represented in Lao NLP research. We construct a text classification dataset to alleviate the resource-scare situation of the Lao language. We present the first transformer-based PTMs for Lao with four versions: BERT-small, BERT-base, ELECTRA-small and ELECTRA-base, and evaluate it over two downstream tasks: part-of-speech tagging and text classification.
  arXiv  Detail & Related papers  (2021-10-12T11:13:07Z)
• 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)
• AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization [13.082435183692393]
  We propose a novel pre-trained language model, referred to as AMBERT (A Multi-grained BERT) For English, AMBERT takes both the sequence of words (fine-grained tokens) and the sequence of phrases (coarse-grained tokens) as input after tokenization. Experiments have been conducted on benchmark datasets for Chinese and English, including CLUE, GLUE, SQuAD and RACE.
  arXiv  Detail & Related papers  (2020-08-27T00:23:48Z)
• Byte Pair Encoding is Suboptimal for Language Model Pretraining [49.30780227162387]
  We analyze differences between unigram LM tokenization and byte-pair encoding (BPE) We find that the unigram LM tokenization method matches or outperforms BPE across downstream tasks and two languages. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
  arXiv  Detail & Related papers  (2020-04-07T21:21:06Z)
• Incorporating BERT into Neural Machine Translation [251.54280200353674]
  We propose a new algorithm named BERT-fused model, in which we first use BERT to extract representations for an input sequence. We conduct experiments on supervised (including sentence-level and document-level translations), semi-supervised and unsupervised machine translation, and achieve state-of-the-art results on seven benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-17T08:13:36Z)

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.