Hewitt 和 Manning 在论文 A Structural Probe for Finding Syntax in Word Representations 中提出了 “结构性探针” 的概念,从经验上来说,将内部表示的空间转换为语言知识的空间是可能实现的。探针识别一种线性变换,在这个变换下,变换表示的 L2 平方距离编码解析树中单词之间的距离,而变换表示的 L2 平方范数编码解...
首先是Internal Representations Tenney et al probe了编码在上下文表示里的语言学知识。主要的工作包括: 在一些辅助任务上(Part-of-Speech, Constituents, Dependencies, Entities, Semantic Role Labelling, Semantic Proto Roles, and Coreference resolutions)比较了一些基于上下文表示的模型(BERT, GPT, ELMO, and CoVe...
For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in ...
International Conference on Learning Representations https://iclr.cc/Conferences/2023 2022.09.21 2023.05.01 ICSE 2023 软件工程 International Conference on Software Engineering https://conf.researchr.org/home/icse-2023 2022.09.01 2023.05.14 SIGKDD 2023 ...
Encoder–decoder approaches . In Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pages 103–111, Doha, Qatar.[5] Ashish Vaswani et al. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30. Curran...
Text representation techniques such as Bag of Words (BoW), TF-IDF, and word embeddings (e.g., Word2Vec, GloVe) translate text into numerical representations that machines can understand. 4. Feature Extraction This step involves identifying significant elements or patterns in the text, such as ...
记录下LSTM的公式,时常温习用。 LSTM 论文:S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 1997. GRU(Gated Recurrent Unit ) 论文出处:Learning phrase representations using RNN encoder-decoder ... NLP学习笔记 text = text.lower() //全部小写 import re text = re.sub...
A landmark in transformer models was Google’s bidirectional encoder representations from transformers (BERT), which became and remains the basis of how Google’s search engine works. Autoregressive models: This type of transformer model is trained specifically to predict the next word in a sequence...
Early 2000s: Machine learning, especially neural networks, becomes increasingly influential in NLP. 2010s: The deep learning revolution significantly advances NLP. State-of-the-Art NLP Models BERT(Bidirectional Encoder Representations from Transformers): Developed by Google, BERT understands context and ...
2019 年,谷歌人工智能部门针对语境化语言表征的自监督学习任务,发布了轻量级的 BERT 模型——ALBERT(论文:《ALBERT: A Lite BERT for Self-supervised Learning of Language Representations》)。该模型主要的改进之处在于减少冗余,并且更高效地分配模型的容量。该方法在12个自然语言处理任务上,都实现了最先进的性能。