iamkissg
  • PaperHighlights
  • 2019
    • 03
      • Not All Contexts Are Created Equal Better Word Representations with Variable Attention
      • Learning Context-Sensitive Word Embeddings with Neural Tensor Skip-Gram Model
      • Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
      • pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
      • Contextual Word Representations: A Contextual Introduction
      • Not All Neural Embeddings are Born Equal
      • High-risk learning: acquiring new word vectors from tiny data
      • Learning word embeddings from dictionary definitions only
      • Dependency-Based Word Embeddings
    • 02
      • Improving Word Embedding Compositionality using Lexicographic Definitions
      • From Word Embeddings To Document Distances
      • Progressive Growing of GANs for Improved Quality, Stability, and Variation
      • Retrofitting Word Vectors to Semantic Lexicons
      • Bag of Tricks for Image Classification with Convolutional Neural Networks
      • Multi-Task Deep Neural Networks for Natural Language Understanding
      • Snapshot Ensembles: Train 1, get M for free
      • EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
      • Counter-fitting Word Vectors to Linguistic Constraints
      • AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling
      • Learning semantic similarity in a continuous space
      • Progressive Neural Networks
      • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
      • Language Models are Unsupervised Multitask Learners
    • 01
      • Querying Word Embeddings for Similarity and Relatedness
      • Data Distillation: Towards Omni-Supervised Learning
      • A Rank-Based Similarity Metric for Word Embeddings
      • Dict2vec: Learning Word Embeddings using Lexical Dictionaries
      • Graph Convolutional Networks for Text Classification
      • Improving Distributional Similarity with Lessons Learned from Word Embeddings
      • Real-time Personalization using Embeddings for Search Ranking at Airbnb
      • Glyce: Glyph-vectors for Chinese Character Representations
      • Auto-Encoding Dictionary Definitions into Consistent Word Embeddings
      • Distilling the Knowledge in a Neural Network
      • Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrin
      • The (Too Many) Problems of Analogical Reasoning with Word Vectors
      • Linear Ensembles of Word Embedding Models
      • Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance
      • Dynamic Meta-Embeddings for Improved Sentence Representations
  • 2018
    • 11
      • Think Globally, Embed Locally — Locally Linear Meta-embedding of Words
      • Learning linear transformations between counting-based and prediction-based word embeddings
      • Learning Word Meta-Embeddings by Autoencoding
      • Learning Word Meta-Embeddings
      • Frustratingly Easy Meta-Embedding – Computing Meta-Embeddings by Averaging Source Word Embeddings
    • 6
      • Universal Language Model Fine-tuning for Text Classification
      • Semi-supervised sequence tagging with bidirectional language models
      • Consensus Attention-based Neural Networks for Chinese Reading Comprehension
      • Attention-over-Attention Neural Networks for Reading Comprehension
      • Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
      • Convolutional Neural Networks for Sentence Classification
      • Deep contextualized word representations
      • Neural Architectures for Named Entity Recognition
      • Improving Language Understanding by Generative Pre-Training
      • A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence C
      • Teaching Machines to Read and Comprehend
    • 5
      • Text Understanding with the Attention Sum Reader Network
      • Effective Approaches to Attention-based Neural Machine Translation
      • Distance-based Self-Attention Network for Natural Language Inference
      • Deep Residual Learning for Image Recognition
      • U-Net: Convolutional Networks for Biomedical Image Segmentation
      • Memory Networks
      • Neural Machine Translation by Jointly Learning to Align and Translate
      • Convolutional Sequence to Sequence Learning
      • An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
      • Graph Attention Networks
      • Attention is All You Need
      • DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding
      • A Structured Self-attentive Sentence Embedding
      • Hierarchical Attention Networks for Document Classification
      • Grammar as a Foreign Language
      • Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
      • Transforming Auto-encoders
      • Self-Attention with Relative Position Representations
    • 1
      • 20180108-20180114
  • 2017
    • 12
      • 20171218-2017124 论文笔记
    • 11
      • 20171127-20171203 论文笔记 1
      • 20171106-20171126 论文笔记
      • 20171030-20171105 论文笔记 1
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  1. 2019
  2. 03

Contextual Word Representations: A Contextual Introduction

Previouspair2vec: Compositional Word-Pair Embeddings for Cross-Sentence InferenceNextNot All Neural Embeddings are Born Equal

Last updated 5 years ago

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论文地址:

要点

如标题所示, 本文要讲的无非 contextual word representations, 但是它并不从 context 讲起, 而是换了个说法, 很清奇.

文章将我们通常意义上所谓的 word, 分成了两类: word token 和 word type. Word token 就是 tokenization 得到的一个个 token; 而 word type, 就是字面的意思, 是一个类型, 是一个更抽象的概念. 但是, 我们所知道的 token 被归到了 word type, 而 word token 是 tokenization 得到的那一个个与 context 息息相关的, 可以说独一无二的 token. 这一点不难理解, 按照这个说法, 就是按字形分类了呗, 所有长得一样的是一个 word type. 就像我们习惯把随单复数变化的单词认为是一个单词, 现在只是换了一个分类准则.

于是, 过去所谓的种种 word representations, 不管是 one-hot, 还是 count-based, 还是 word embeddings, 都是 word type representations. 不管对应的 word 出现在哪个句子中的哪个位置, 它的 representations 不变. 怎么被作者说通了呢- -.

而 word token representations, 或者换个更耳熟能详的名字, contextual word representations, 一个 word token 在不同 context 中含义不同, 取决于 context. 对于一词多义的情况, 尤其如此, 比如 bank, 一作银行一作岸, 联系上下文就很清楚是什么意思了.

我和本文的作者一样, 硬是就一个很清晰的概念扯了不少, 但是以本文提供的 word type 和 word token 的视角来看, 当念及一句话中的一个单词, 你可能会首先想到 word token, 自然就强化了上下文的作用, 而且也不必转念去思考什么 context; 而念及一个孤零零的单词, 可能就首先想到 word type. 本文带给我这种思维上的转换, 就像打通了任督二脉一样, 让我感觉很舒服. 如果读者没有这种感受, 恕我笔拙, 请看原文.

文章的最后有点口号式, 抄一下:

  • Language is a lot more than words.

  • Natural language processing is not a single problem.

说白了, 就是任重而道远.

https://arxiv.org/abs/1902.06006