3 research outputs found
On How Users Edit Computer-Generated Visual Stories
A significant body of research in Artificial Intelligence (AI) has focused on
generating stories automatically, either based on prior story plots or input
images. However, literature has little to say about how users would receive and
use these stories. Given the quality of stories generated by modern AI
algorithms, users will nearly inevitably have to edit these stories before
putting them to real use. In this paper, we present the first analysis of how
human users edit machine-generated stories. We obtained 962 short stories
generated by one of the state-of-the-art visual storytelling models. For each
story, we recruited five crowd workers from Amazon Mechanical Turk to edit it.
Our analysis of these edits shows that, on average, users (i) slightly
shortened machine-generated stories, (ii) increased lexical diversity in these
stories, and (iii) often replaced nouns and their determiners/articles with
pronouns. Our study provides a better understanding on how users receive and
edit machine-generated stories,informing future researchers to create more
usable and helpful story generation systems.Comment: To appear in CHI'19 Late-Breaking Work on Human Factors in Computing
Systems (CHI LBW 2019), 201
Visual Story Post-Editing
We introduce the first dataset for human edits of machine-generated visual
stories and explore how these collected edits may be used for the visual story
post-editing task. The dataset, VIST-Edit, includes 14,905 human edited
versions of 2,981 machine-generated visual stories. The stories were generated
by two state-of-the-art visual storytelling models, each aligned to 5
human-edited versions. We establish baselines for the task, showing how a
relatively small set of human edits can be leveraged to boost the performance
of large visual storytelling models. We also discuss the weak correlation
between automatic evaluation scores and human ratings, motivating the need for
new automatic metrics.Comment: Accepted by ACL 201
AfriKI: Machine-in-the-Loop Afrikaans Poetry Generation
This paper proposes a generative language model called AfriKI. Our approach
is based on an LSTM architecture trained on a small corpus of contemporary
fiction. With the aim of promoting human creativity, we use the model as an
authoring tool to explore machine-in-the-loop Afrikaans poetry generation. To
our knowledge, this is the first study to attempt creative text generation in
Afrikaans.Comment: Accepted to EACL 2021 Workshop on AfricaNLP (non-archival