NLP Embedding

Requirement: Tensorflow 1.14, Tensorboard 1.14

The code is not rigorously tested, if you find a bug, welcome PR ^_^ ~

Model List

1. Word2Vec: sogou新闻数据
2. Fasttext: quora kaggle 分类数据
3. Doc2Vec[PV-DBOW/PV-DM]: sogou新闻数据
4. skip-thought: bookcorpus爬虫数据
5. quick-thought: bookcorpus爬虫数据
6. CNN-LSTM: bookcorpus爬虫数据
7. transformer: WMT英翻中任务

Paper List

词向量

• [Word2Vec] Distributed Representations of Words and Phrases and their Compositionality (Google 2013)
• [Word2Vec] Efficient Estimation of Word Representations in Vector Space (Google 2013)
• [Word2Vec] word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method (2014)
• [Word2Vec] word2vec Parameter Learning Explained (2016)
• [Fasttext] Enriching Word Vectors with Subword Information (Facebook 2017)
• [Fasttext] Bag of Tricks for Efficient Text Classification (Facebook 2016)
• [Glove] Global Vectors for Word Representation (2014)
• [ELMo] Deep contextualized word representations (2018)

文本向量

• [Doc2vec] Distributed Representations of Sentences and Documents (Google 2014)
• [Doc2vec] A SIMPLE BUT TOUGH-TO-BEAT BASELINE FOR SEN- TENCE EMBEDDINGS (2017)
• [Encoder-Decoder: Skip-Thought] Skip-Thought Vectors (2015)
• [Encoder-Decoder: Skip-Thought] Rethinking Skip-thought- A Neighborhood based Approach (2017)
• [Encoder-Decoder: CNN-LSTM]Learning Generic Sentence Representations Using Convolutional Neural Networks (2017)
• [Encoder-Decoder: Quick-Thought] Quick-Thought: AN EFFICIENT FRAMEWORK FOR LEARNING SENTENCE REPRESENTATIONS (Google 2018)
• [Transformer] Attention is all you need (2017)
• [FastSent|DVAE]Learning Distributed Representations of Sentences from Unlabelled Data (2016)
• [Siamese] Learning Text Similarity with Siamese Recurrent Networks (2016)
• [InferSent] Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (2018)
• [GenSen] LEARNING GENERAL PURPOSE DISTRIBUTED SENTENCE REPRESENTATIONS VIA LARGE SCALE MULTITASK LEARNING (2018)
• [USE] Universal Sentence Encoder (Google 2018)
• [ULMFit] Universal Language Model Fine-tuning for Text Classification (fastai 2018)
• [GPT] Improving Language Understanding by Generative Pre-Training (openai 2018)
• [Bert] Pre-training of Deep Bidirectional Transformers for Language Understanding（Google 2019)
• [Sentence-BERT] Sentence Embeddings using Siamese BERT-Networks (2019)
• [Bert-flow] On the Sentence Embedding from Pre-trained Language Model(2020)
• [Representation] Fine-Grained Analysis of Sentence Embedding Using Auxiliary Prediction Tasks (2017)
• [Representation] What you can cram into a single vector: Probing Sentence Embedding for linguistic properties(2018)
• [Representation] Assessing Composition in Sentence Vector Representations (2018)

Blog

• 无所不能的Embedding1 - Word2vec模型详解&代码实现
• 无所不能的Embedding2 - FastText词向量&文本分类
• 无所不能的Embedding3 - word2vec->Doc2vec[PV-DM/PV-DBOW]
• 无所不能的Embedding4 - skip-thought & tf-Seq2Seq源码解析
• 无所不能的Embedding5 - skip-thought的兄弟们[Trim/CNN-LSTM/quick-thought]
• 无所不能的Embedding6 - 跨入Transformer时代～模型详解&代码实现
• 无所不能的Embedding7 - 探索通用文本表达[FastSent/InferSent/GenSen/USE]