ACL Findings 2021 机器翻译论文概览 Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade开源代码地址类型:长文作者机构:FAIR,CMU方向:非自回归研究背景:使用深层编码器浅层解码器的普通AT Transfo… Timso...发表于机翻论文笔... NAACL 2022 机器翻译论文概览 (含Findings) Language Model ...
后续可能会出现有趣大会之*ACL,EMNLP,COLING等。 What will the series of articles cover and what benefits does it bring me?These articles will present a selection of studies from the top conferences withinterestingideas or tasks, followed by very short explanations. There will not be in-depth exp...
有趣大会 · ACL2022 (Findings篇)mp.weixin.qq.com/s/688c_nViJDmm0qK7Dh48VQ 什么是有趣“大会”? 它并不是一个和官方相关,真正的大会。它会是以趣味为导向的一系列文章,可以用放松、不太正式的心态去阅读它。 What is the series of "Interesting"? It will be a series of entertainment-orient...
:coconut: Code & Data for Comparative Opinion Summarization via Collaborative Decoding (Iso et al; Findings of ACL 2022) - megagonlabs/cocosum
🦮 Code and pretrained models for Findings of ACL 2022 paper "LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval" - JetRunner/LaPraDoR
This cross-sectional study aimed at exploring the frequency and extent of knee joint lesions associated with delayed treatment of anterior cruciate ligament (ACL) injury. It enrolled 300 patients from 2020 to 2022 who were subjected to arthroscopy for anterior cruciate ligament reconstruc...
new domains with limited resources. Paper: https://aclanthology.org/2022.findings-emnlp.468.pdf 展开更多 自然语言处理 人工智能 表征学习 【2025版】不愧是吴恩达教授!一口气讲透CNN、RNN、GAN、GNN、DQN、Transformer、LSTM等八大深度学习神经网络算法!简直不要太爽!
These permissions seem correct at face value, but when we look at the ACL of one of the files we actually found: LocalSystem – Full Control Administrators – Full Control NetworkService – Full Control LocalService – Full Control If you look at a default Exchange installation you will also ...
Morimoto LR, Kase DT, Esmanhotto PG, Maciel MA, Augusto ACL, Catricala PF, et al. Imaging assessment of nontraumatic pathologic conditions at the craniovertebral junction: a comprehensive review. Radiographics. 2024;44(5): e230137. Article PubMed Google Scholar Mizutomi K, Ryu Y, Sugimor...
我们在每个领域上都挑选了一个对应的下游任务,分别是 MNLI (WB domain),HyperPartisan (Ns domain), Helpfulness (Rev domain), ChemProt (Bio domain), ACL-ARC (CS domain),并用不同方法预训练的BERT模型在这些任务上进行微调。 可以看到,在各个预训练阶段后,由于更好的记住了学过的知识,ELLE在各个领域下游...