Papers I Read Notes and Summaries

Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks


  • This workshop paper explores the problem of style transfer in natural language generation (NLG).
  • One possible manifestation would be rewriting technical articles in an easy-to-understate manner.


  • Identifying relevant stylistic cues and using them to control text generation in NLG systems.
  • Absence of a large amount of training data.


  • Using Recurrent Neural Networks (RNNs) to disentangle the style from semantic content.
  • Autoencoder model with two components - one for learning style and another for learning content.
  • This allows for “style” component to be replaced while keeping the “content” component same, resulting in a style transfer.
  • One way to think about this is - the encoder generates a 100-dimensional vector. In this, the first 50 entries, correspond to the “style” component and remaining to the “content” component.
  • The proposal is that the loss function should be modified to include a cross-covariance term for ensuring disentanglement.
  • I think one way of doing this is to have two loss functions:
    • The first loss function ensures that the input sentence is decoded properly into the target sentence. This loss is computed for each sentence.
    • The second loss ensures that the first 50 entries across all the encoded represenations are are correlated. This loss operates at the batch level.
    • The total loss is the weighted sum of these 2 losses.

Possible Datasets

Possible Metrics

  • Soundness - is the generated text entailed with the input sentence.
  • Coherence - free of grammatical errors, proper word usage etc.
  • Effectiveness - how effective was the style transfer
  • Since some of the metrics are subjective, human evaluators also need to be employed.