LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization

• URL: http://arxiv.org/abs/2108.00801v2
• Date: Tue, 3 Aug 2021 06:30:43 GMT
• Title: LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization
• Authors: Weidong Guo, Mingjun Zhao, Lusheng Zhang, Di Niu, Jinwen Luo, Zhenhua Liu, Zhenyang Li and Jianbo Tang
• Abstract summary: We propose a simple yet effective pre-training method named LICHEE to efficiently incorporate multi-grained information of input text. Our method can be applied to various pre-trained language models and improve their representation capability.
• Score: 19.89228774074371
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Language model pre-training based on large corpora has achieved tremendous success in terms of constructing enriched contextual representations and has led to significant performance gains on a diverse range of Natural Language Understanding (NLU) tasks. Despite the success, most current pre-trained language models, such as BERT, are trained based on single-grained tokenization, usually with fine-grained characters or sub-words, making it hard for them to learn the precise meaning of coarse-grained words and phrases. In this paper, we propose a simple yet effective pre-training method named LICHEE to efficiently incorporate multi-grained information of input text. Our method can be applied to various pre-trained language models and improve their representation capability. Extensive experiments conducted on CLUE and SuperGLUE demonstrate that our method achieves comprehensive improvements on a wide variety of NLU tasks in both Chinese and English with little extra inference cost incurred, and that our best ensemble model achieves the state-of-the-art performance on CLUE benchmark competition.

Related papers

• A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models [39.35525969831397]
  This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task. Experiments on five public datasets demonstrate that our approach significantly improves LLM performance.
  arXiv  Detail & Related papers  (2024-10-05T04:06:56Z)
• CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning [4.004641316826348]
  We introduce a novel language-image Contrastive Learning method with an Efficient large language model and prompt Fine-Tuning (CLEFT) Our method demonstrates state-of-the-art performance on multiple chest X-ray and mammography datasets. The proposed parameter efficient framework can reduce the total trainable model size by 39% and reduce the trainable language model to only 4% compared with the current BERT encoder.
  arXiv  Detail & Related papers  (2024-07-30T17:57:32Z)
• Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet? [82.02076369811402]
  Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning. We present an extensive and systematic comparison of the three approaches, testing them on 6 high- and low-resource languages, three different NLU tasks, and a myriad of language and domain setups. Our observations show that supervised instruction tuning has the best trade-off between performance and resource requirements.
  arXiv  Detail & Related papers  (2024-03-04T10:48:13Z)
• 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)
• Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling [101.74165219364264]
  Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages. In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap. Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel
  arXiv  Detail & Related papers  (2022-04-11T15:55:20Z)
• Differentiable Prompt Makes Pre-trained Language Models Better Few-shot Learners [23.150999852147283]
  This study proposes a novel pluggable, and efficient approach named DifferentiAble pRompT (DART) It can convert small language models into better few-shot learners without any prompt engineering. A comprehensive evaluation of standard NLP tasks demonstrates that the proposed approach achieves a better few-shot performance.
  arXiv  Detail & Related papers  (2021-08-30T12:29:25Z)
• 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)
• Mixed-Lingual Pre-training for Cross-lingual Summarization [54.4823498438831]
  Cross-lingual Summarization aims at producing a summary in the target language for an article in the source language. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks like translation and monolingual tasks like masked language models. Our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.
  arXiv  Detail & Related papers  (2020-10-18T00:21:53Z)
• InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training [135.12061144759517]
  We present an information-theoretic framework that formulates cross-lingual language model pre-training. We propose a new pre-training task based on contrastive learning. By leveraging both monolingual and parallel corpora, we jointly train the pretext to improve the cross-lingual transferability of pre-trained models.
  arXiv  Detail & Related papers  (2020-07-15T16:58:01Z)
• DIET: Lightweight Language Understanding for Dialogue Systems [0.0]
  Large-scale pre-trained language models have shown impressive results on language understanding benchmarks like GLUE and SuperGLUE. We introduce the Dual Intent and Entity Transformer (DIET) architecture, and study the effectiveness of different pre-trained representations on intent and entity prediction.
  arXiv  Detail & Related papers  (2020-04-21T12:10:48Z)

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.