ACL 2022：Graph Pre-training for AMR Parsing and Generation - 代码天地

ACL 2022：Graph Pre-training for AMR Parsing and Generation

企业开发 2023-06-04 21:55:37 阅读次数: 0

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转载自blog.csdn.net/qq_45041871/article/details/130967902

ACL 2022：Graph Pre-training for AMR Parsing and Generation

ACL 2019 - AMR Parsing as Sequence-to-Graph Transduction

One SPRING to Rule Them Both Symmetric AMR Semantic Parsing and Generation without Complex Pipeline

ACL2023 - AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model

论文阅读总结：UniLM(Unified Language Model Pre-training for Natural Language Understanding and Generation)

[文献阅读]——MASS: Masked Sequence to Sequence Pre-training for Language Generation

BLIP:Bootstrapping language-image pre-training for unified VL understanding and generation

【论文笔记】DialoGPT:Large-Scale Generative Pre-training for Conversational Response Generation

A Graph-to-Sequence Model for AMR-to-Text Generation读书笔记

Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base【论文笔记】

A Transition-based Algorithm for AMR Parsing

论文阅读 | ACL2019 Exploring Pre-trained Language Models for Event Extraction and Generation

Unsupervised pre-training

Rethinking ImageNet Pre-training

《Rethinking ImageNet Pre-training》理解

对Rethinking ImageNet Pre-training的理解

Pre-training model(待更)

Transfer Learning、Finetuning和Pre-training

Graph R-CNN for Scene Graph Generation

ACL2023 - An AMR-based Link Prediction Approach for Document-level Event Argument Extraction

Scene Graph Generation by Iterative Message Passing解读

[Current]Notes for Scene Graph Generation Project

Scene Graph Generation by Iterative Message Passing

Unbiased Scene Graph Generation in Videos论文讲解

BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding

Rethinking ImageNet Pre-training 重新审视Imagenet预训练

【论文阅读】Advances in Pre-Training Distributed Word Representations

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

The `weights` argument should be either random initialization or pre-training on ImageNet

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization

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