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Reproducing the Metric-Based Evaluation of a Set of Controllable Text Generation Techniques

arXiv 2405.07875 ↗First posted 13 May 2024cs.CLcited by 0

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Abstract (arXiv)

Rerunning a metric-based evaluation should be more straightforward, and results should be closer, than in a human-based evaluation, especially where code and model checkpoints are made available by the original authors. As this report of our efforts to rerun a metric-based evaluation of a set of single-attribute and multiple-attribute controllable text generation (CTG) techniques shows however, such reruns of evaluations do not always produce results that are the same as the original results, and can reveal errors in the reporting of the original work.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 13 May 2024.

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6 of 21 references.

References · 6

  1. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  2. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,068
  3. 2021A Systematic Review of Reproducibility Research in Natural Language Processingcited by 34
  4. 2022A Distributional Lens for Multi-Aspect Controllable Text Generationcited by 33
  5. 2021Attribute Alignment: Controlling Text Generation from Pre-trained Language Modelscited by 27
  6. 2022Controllable Text Generation via Probability Density Estimation in the Latent Spacecited by 27

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