English

Machine-in-the-Loop Rewriting for Creative Image Captioning

Computation and Language 2022-05-10 v2

Abstract

Machine-in-the-loop writing aims to enable humans to collaborate with models to complete their writing tasks more effectively. Prior work has found that providing humans a machine-written draft or sentence-level continuations has limited success since the generated text tends to deviate from humans' intention. To allow the user to retain control over the content, we train a rewriting model that, when prompted, modifies specified spans of text within the user's original draft to introduce descriptive and figurative elements locally in the text. We evaluate the model on its ability to collaborate with humans on the task of creative image captioning. On a user study through Amazon Mechanical Turk, our model is rated to be more helpful than a baseline infilling language model. In addition, third-party evaluation shows that users write more descriptive and figurative captions when collaborating with our model compared to completing the task alone.

Keywords

Cite

@article{arxiv.2111.04193,
  title  = {Machine-in-the-Loop Rewriting for Creative Image Captioning},
  author = {Vishakh Padmakumar and He He},
  journal= {arXiv preprint arXiv:2111.04193},
  year   = {2022}
}

Comments

To appear at NAACL 2022

R2 v1 2026-06-24T07:29:43.495Z