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  • 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

Not All Neural Embeddings are Born Equal

PreviousContextual Word Representations: A Contextual IntroductionNextHigh-risk learning: acquiring new word vectors from tiny data

Last updated 5 years ago

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论文地址:

要点

这篇小短文主要验证了两类完全不同的方法习得的 word embeddings 具有非常不同的属性.

一类是用单语言, 基于 language model 及其变种学习的词向量, GloVe/CW/Skipgram 都属于此类; 另一类是从翻译模型中习得的 encoder 的 word embeddings.

结果表明, 基于翻译的 word emeddings 在 word similarity 上表现得好得多, 而基于单语言的 word embeddings 在 relatedness 上迎头赶上. 而且即使加大单语言的语料, 不能改变现状.

而 word analogy 的结果现实, 基于翻译的 word embeddings 在 semantic 型任务上被按在地上摩擦, 但是在 syntactic 任务上, 反过来将其他 word embeddings 完爆了.

综上, 基于翻译的模型对于 word similarity (相似性) 和 syntactic (句法) 的学习更有效. 而单语模型更善于捕获相关性和语义属性.

翻译时, 目标语言中一个单词对应原语言中多个单词, 会鼓励这些单词相互靠近; 因为原语言中的词基本不会被翻译成目标语言中的反义词 (虽然在单语环境下, 反义词可能会有很多相似的上下文). 所以基于翻译的 word embeddings 会强化这类属性, 使得向量空间更分明.

备注

就本文的一个启示是, 用预训练的模型作为 NMT 的 embedding layer, 固定该层是绝对不恰当的做法. 这个结论应该可以推广到其他 downstream tasks.

https://arxiv.org/abs/1410.0718