Text representation isNLPThe mission is very basic and at the same time a very important part. This article will introduce the history of text representation and the advantages and disadvantages of each method.
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Take a look at past 20 years with ACL papers,NLP The development trend.
We'll start with the simplest method and move on to more subtle solutions such as feature engineering, word vectors and deep learning.
CMU and Google’s new XLNet surpassed 20 tasks BERT Performance and achieved the best results on 18 tasks! What's even more exciting is that XLNet has now opened training code and large pre-training models.
We will introduce you quicklyNLPThe main technical methods of 3 in it, and how we use them to build great machines!
2018 10 At the beginning of the month, Google AI proposed a new contextual word representation -BERT feature. This paper introduces the BERT features and briefly analyzes the interpretability of BERT features.
Dr. Zhou Ming, Associate Dean of Microsoft Research Asia and ACL Chairman, accepted an interview with “Heart of the Machine” and discussed it from the macro level and technical level.NLPResearch progress and future development trends. The following is a selection of interviews with Dr. Zhou Ming.
Deep Learning Indaba 2018 is a deep learning summit hosted by DeepMind and was held in Stellenbosch, South Africa during the 9 month of this year. This article is based on expert interviews and panel discussions at the time, mainly discussingNLP4 major open issues in the domain.
Recently, Shannon Technology published a research and proposed a Chinese glyph vector Glyce. The research is based on the evolution of Chinese characters, using a variety of Chinese characters and various writing styles, designed for Chinese pictographic characters. CNN Architecture - Tian Character Bank CNN. Glyce in 13 (almost all) Chinese NLP The task has achieved the best performance at the moment.