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. 2018
  2. 6

Convolutional Neural Networks for Sentence Classification

PreviousBaseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling MechanismsNextDeep contextualized word representations

Last updated 5 years ago

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论文地址:

TL;DR

(14 年的论文) 提出了用于句子分类的单卷积层的 CNN 模型, 简单而强大.

Key Points

  • 本文针对句子分类任务, 提出了一个极简 CNN 模型, 仅包含: embedding Layer x1, Conv Layer x1, Pooling Layer x1, FC x1, output layer x1.

  • 不同于 CV 中使用的通常为 nxn 的 filter, CNN in NLP 使用的是横跨多个 word vector, 长度等同于 embeddingdim 的 filter. 一个 filter 对整个句子的 embedding matrix 计算一遍卷积之后, 得到特征向量: $\mathbf{c}=[c_1, c_2, \dots, c{n-h+1}]$.

  • 使用 max-over-time pooling, 实际就是从一个 filter 得到的特征向量中取最大值: $\hat{c}=max(\mathbf{c})$.

  • 将多个 filter 得到的 $\hat{c}$ 们拼接, 构成下一层 FC 的输入. FC 中使用了 dropout, 并对参数做了 l2-norm.

  • 文中还进行了一个多通道 word vector 的实验, 具体而言就是: 在输入层使用多个 embedding layer, 记为 multi-channel, filter 再对多个 embedding layer 做卷积, 就像 CV 一样. (如下)

  • 文中采用了 4 种不同的 word vector 策略: 不使用预训练的, 使用预训练的但不 fine-tune, 使用预训练的并 fine-tune, 双通道(都使用预训练的, 一组不 fine-tune, 一组 fine-tune). 结果:

    • 多通道与单通道各有胜负;

    • fine-tune 通常能带来进一步的性能提升.

  • 文章在 7 个不同的句子分类任务上进行了实验, 而本文提出的模型及其变种在 4 个上取得了当时 SOTA 的结果, 其中 3 个优势巨大. 证明了模型的有效性.

Notes/Questions

  • Multi-channel in NLP 有点意思. 这说明道理都是差不多的, 知识是可以迁移的. Attention is All you Need 中的 Multi-Head 类似于 Multi-channel.

http://www.aclweb.org/anthology/D14-1181
CNN4SC_architecture.png