LDP: Generalizing to Multilingual Visual Information Extraction by Language Decoupled Pretraining

• URL: http://arxiv.org/abs/2412.14596v1
• Date: Thu, 19 Dec 2024 07:31:40 GMT
• Title: LDP: Generalizing to Multilingual Visual Information Extraction by Language Decoupled Pretraining
• Authors: Huawen Shen, Gengluo Li, Jinwen Zhong, Yu Zhou,
• Abstract summary: We propose a multilingual training paradigm LDP (Language Decoupled Pre-training) for better utilization of monolingual pre-training data. Our proposed model LDM is first pre-trained on the language-independent data, where the language knowledge is decoupled by a diffusion model, and then the LDM is fine-tuned on the downstream languages.
• Score: 2.6638517946494535
• License:
• Abstract: Visual Information Extraction (VIE) plays a crucial role in the comprehension of semi-structured documents, and several pre-trained models have been developed to enhance performance. However, most of these works are monolingual (usually English). Due to the extremely unbalanced quantity and quality of pre-training corpora between English and other languages, few works can extend to non-English scenarios. In this paper, we conduct systematic experiments to show that vision and layout modality hold invariance among images with different languages. If decoupling language bias from document images, a vision-layout-based model can achieve impressive cross-lingual generalization. Accordingly, we present a simple but effective multilingual training paradigm LDP (Language Decoupled Pre-training) for better utilization of monolingual pre-training data. Our proposed model LDM (Language Decoupled Model) is first pre-trained on the language-independent data, where the language knowledge is decoupled by a diffusion model, and then the LDM is fine-tuned on the downstream languages. Extensive experiments show that the LDM outperformed all SOTA multilingual pre-trained models, and also maintains competitiveness on downstream monolingual/English benchmarks.

Related papers

• PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment [68.20851615263953]
  Large language models demonstrate reasonable multilingual abilities, despite predominantly English-centric pretraining. The spontaneous multilingual alignment in these models is shown to be weak, leading to unsatisfactory cross-lingual transfer and knowledge sharing. We propose PreAlign, a framework that establishes multilingual alignment prior to language model pretraining.
  arXiv  Detail & Related papers  (2024-07-23T06:59:53Z)
• A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives [13.581385765600265]
  Pretrained language models (PLMs) display impressive performances and have captured the attention of the NLP community. This paper proposes a comparison of multilingual pretraining objectives in a controlled methodological environment.
  arXiv  Detail & Related papers  (2024-07-22T09:16:30Z)
• Do Multilingual Large Language Models Mitigate Stereotype Bias? [9.31741279000585]
  This study systematically trains six LLMs of identical size and architecture in English, German, French, Italian, and Spanish. We observe that multilingual models achieve not only lower bias but also superior prediction accuracy when compared to monolingual models.
  arXiv  Detail & Related papers  (2024-07-08T08:46:50Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• Sabi\'a: Portuguese Large Language Models [14.801853435122908]
  We show that monolingual pretraining on the target language significantly improves models already extensively trained on diverse corpora. Few-shot evaluations on Poeta, a suite of 14 Portuguese datasets, reveal that our models outperform English-centric and multilingual counterparts by a significant margin.
  arXiv  Detail & Related papers  (2023-04-16T20:11:19Z)
• Language Model Pre-Training with Sparse Latent Typing [66.75786739499604]
  We propose a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge.
  arXiv  Detail & Related papers  (2022-10-23T00:37:08Z)
• Generalizing Multimodal Pre-training into Multilingual via Language Acquisition [54.69707237195554]
  English-based Vision-Language Pre-training has achieved great success in various downstream tasks. Some efforts have been taken to generalize this success to non-English languages through Multilingual Vision-Language Pre-training. We propose a textbfMultitextbfLingual textbfAcquisition (MLA) framework that can easily generalize a monolingual Vision-Language Pre-training model into multilingual.
  arXiv  Detail & Related papers  (2022-05-29T08:53:22Z)
• MergeDistill: Merging Pre-trained Language Models using Distillation [5.396915402673246]
  We propose MergeDistill, a framework to merge pre-trained LMs in a way that can best leverage their assets with minimal dependencies. We demonstrate the applicability of our framework in a practical setting by leveraging pre-existing teacher LMs and training student LMs that perform competitively with or even outperform teacher LMs trained on several orders of magnitude more data and with a fixed model capacity.
  arXiv  Detail & Related papers  (2021-06-05T08:22:05Z)
• Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
  We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
  arXiv  Detail & Related papers  (2020-10-21T17:01:23Z)
• 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)

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.