READING NOTES: Baseline needs more love (Shen

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Reading notes of Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

Main Idea:

Point to point comparision between Simple word embedding based models(pooling strategies) and RNN/CNNs on 17 datasets and three tasks:

  1. long document classification
  2. text sequence matching
  3. short text tasks (classification & matching)


  • Pooling strategies:
    1. average pooling: Average of word embeddings for input sequence.
    2. max pooling on fix dimension of word embedding matrix –> most salient features
    3. hierarchical pooling –> abstract and keep spatial info
      • average pooling in window of length \(n\) =>
      • max-pooling on features from all windows
    4. concat features extracted from first two
  • Parameters and speed: faster than CNN/RNN by a factor of \(nd\) or \(d\). (ok now seems kramdown works fine)

Experiment details:

  • Embeddings: Use GloVe embeddings or refined embeddings with MLP
  • Method: SWEM-based v.s. CNN/RNN/LSTM
  • Tasks:
    • document categorization: topic categorization; sentiment analysis, ontology classification.
    • sentence matching
    • sequence tagging(CoNLL2000, CoNLLL2003 NER)


SWEM-based methods with poolings strategies, especially hierarchical pooling, has comparable/better performance and is more computationally efficient. Not as good on classifying short sentences.

Discussion on word-order features

The paper has an interesting discussion on the importance of word-order features, and perform an experiment by shuffling words in training, and keeping word order in test set => LSTM has comparable accuracies on Yahoo and SNLI to unshuffeled training data!

Sentiment analysis is more sensitive to word-order features than other tasks.

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