Chicken Chicken Chicken: Chicken Chicken

AAAS 2007

  • Hits close to home.
  • We Koreans love our fried chicken.
  • Wish we had Chick-fil-A though.

Reflections on Trusting Trust


  • Ken Thompson's Turing award acceptance speech.
  • Presents a way to plant a Trojan in the complier.
  • You build a faulty compiler A, and with it compile another compiler B. Distribute B.
  • An interesting and fairly short read. Highly recommended!

Do sequence-to-sequence VAEs learn global features of sentences?

ACL 2020

  • Short answer: No.
  • Encoder memorizes tokens toward the start and the end. This is measured by distribution of reconstruction loss along sequence length.
  • In other words, seq2seq VAEs tend to learn local features.
  • Most VAE LMs in literature report low KL values in their latent structure. We don't yet know if the reported values are acceptable or not.
  • Replacing LSTM with bag of words helps VAE LMs learn global features and decrease first word memorization.
  • Eliminating posterior collapse in VAEs is not sufficient. LMs must learn global features!

Open Korean Corpora: A Practical Report

NLP-OSS Workshop, ACL 2020

  • A survey on Korean NLP datasets for a variety of tasks (32 corpora).
  • Document will be updated on arxiv as more datasets become available.

Transformer-based Conditional Variational Autoencoder for Controllable Story Generation


  • Another rarely seen Transformer VAE.
  • Follows the same latent injection scheme as OPTIMUS.
  • SOTA in two controlled story generation datasets: WritingPrompts and WikiPlots.
  • Compares perplexity, ROUGE-1, ROGUE-2, and ROGUE-L.

A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text

ACL 2019

  • Yet again, we want to prevent posterior collapse in text VAEs.
  • KL thresholding combined with encoder pretraining (training with AE objective and resetting the decoder) was the most effective in language modeling.
  • However, encoder pretraining also exhibits posterior collapse if without KL annealing.
  • Better ELBO does not necessarily mean better latent representation quality, as measured by mutual information or active units.

On Posterior Collapse and Encoder Feature Dispersion in Sequence VAEs


  • Normally, we pass the last hidden state of LSTM encoders to the decoder in text VAEs.
  • This results in a dull latent space.
  • If we mean-pool or max-pool from all encoder hidden states, we can mitigate posterior collapse.
  • Somewhat reminiscent of Ray Mooney's famous quote.

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nlp, papers

Last Update: July 04, 2024