1. Recent advances in natural language processing, i.e. the automatic analysis of natural language by computer algorithms, have greatly impacted the study of brain activity during sentence comprehe...
deep learning models can useunsupervised learning. With unsupervised learning, deep learning models can extract the characteristics, features and relationships they need to make accurate outputs from raw, unstructured data. Additionally, these models can even evaluate and refine their outputs for increased...
multilingualnlpbenchmarkmachine-learningdeep-learningretrievalmltransformersefficientminerclassificationgenerationlanguage-modelminersdeep-learning-modelssentence-transformerssemantic-retrievallarge-language-modelsllm UpdatedOct 3, 2024 Python mike-gimelfarb/cascade-correlation-neural-networks ...
TRANSFORMER modelsDEEP learningDISABILITIESLexical Simplification (LS) is the task of substituting complex words within a sentence for simpler alternatives while maintaining the sentence's original meaning. LS is the lexical component of Text Simplification (TS) systems with the aim of improving ...
DSSM: Learning deep structured semantic models for web search 1.背景 DSSM是Deep Structured Semantic Model的缩写,即我们通常说的基于深度网络的语义模型,其核心思想是将query和doc映射到到共同维度的语义空间中,通过最大化query 73620 『 DSSM』A Multi-View Deep Learning A...
to a probability distribution that depends on the English sentence. This rather naive way of performing machine translation has quickly become competitive with the state-of-the-art, and this raises serious doubts about whether understanding a sentence requires anything like the internal symbolic ...
本文重点讲概括型摘要生成系统的算法思想和tensorflow实战, 算法思想源于A Neural Attention Model for Abstractive Sentence Summarization这篇论文. 本文希望帮助读者详细的解析算法的原理, 再结合github上相关的开源项目textsum讲解工程上的实际应用.本文由PPmoney大数据算法团队撰写,PPmoney是国内领先的互联网金融公司,旗下...
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other do...
BERT(Bidirectional Encoder Representation from Transformers)是一个迁移能力很强的通用语义表示模型, 以 Transformer 为网络基本组件,以双向 Masked Language Model和 Next Sentence Prediction 为训练目标,通过预训练得到通用语义表示,再结合简单的输出层,应用到下游的 NLP 任务,在多个任务上取得了 SOTA 的结果。
NSP,Next Sentence Prediction。许多重要的下游任务譬如QA、NLI需要语言模型理解两个句子之间的关系,而传统的语言模型在训练的过程没有考虑句对关系的学习。NSP,预测下一句模型,增加对句子A和B关系的预测任务,50%的时间里B是A的下一句,分类标签为IsNext,另外50%的时间里B是随机挑选的句子,并不是A的下一句,分类标...