Papers with pre-training model
Pre-training Methods for Neural Machine Translation (2021.acl-tutorials)
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| Challenge: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Approach: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Outcome: | This tutorial explains how to make the most of pre-training for neural machine translation. |
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
| Challenge: | Existing work on pre-training models have shown that it is important to use a framework to deploy various pre- training models efficiently. |
| Approach: | They propose an assemble-on-demand pre-training toolkit that assembles pre-trained models on demand and encapsulates them with rich modules. |
| Outcome: | The proposed framework can reproduce state-of-the-art models or develop models that remain unexplored. |
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation (D19-1)
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| Challenge: | Existing approaches to build initial unsupervised machine translation models with cross-lingual n-gram embeddings are inexplicit and limited. |
| Approach: | They propose a cross-lingual pre-training method that incorporates cross-linguistic training signals into pre-trained models by randomly choosing source n-grams in the input text stream. |
| Outcome: | The proposed method significantly improves the performance of unsupervised machine translation. |
Geo-BERT Pre-training Model for Query Rewriting in POI Search (2021.findings-emnlp)
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| Challenge: | Existing methods to solve the word mismatch between queries and documents are often inadequate to integrate geographic information into the pre-training model. |
| Approach: | They propose to train a pre-training model to integrate semantics and geographic information in the pre-trained representations of POIs. |
| Outcome: | The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services. |
Decouple knowledge from paramters for plug-and-play language modeling (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have made impressive results in a wide range of NLP tasks. |
| Approach: | They propose a pre-training model with editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’. |
| Outcome: | The proposed model decouples the knowledge storage from model parameters with an editable and scalable key-value memory and leverages knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’. |