For some insane reason, my team submitted 7 papers to the NAACL Workshop on Narrative Understanding.

Even more insane: all seven were accepted!
1. Fabula Entropy Indexing: Objective Measures of Story Coherence
@lcastricato @spencerfrazier @JonathanBalloch

A new way to OBJECTIVELY measure the coherence of story generation systems. Grounded in narratology and validated in controlled studies
2. Towards a Formal Model of Narratives
@lcastricato @recardona @DavidThue

A narratological theory that makes narratives mathematical. This is what makes the above evaluation method work. In other words, a practical theory.
3. Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes

It would be great to have topic and style knobs on story generators. Now they do.
4. Telling Stories through Multi-User Dialogue by Modeling Character Relations

Multi-task training for AI agents that can stay in character and perform 1st- and 2nd-person dialogue in D&D role-playing games
5. Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
@beckypeng6 @sarahwiegreffe @Sylvia_Sparkle

Using commonsense reasoning to guide a neural story generator.

(We'll have more to say about the importance of _reader models_ later, stay tuned)
6. Tell Me A Story Like I’m Five: Story Generation via Question Answering
@lcastricato @spencerfrazier @JonathanBalloch

Neural networks are great language generators. But maybe we are using them wrong when generating stories?
7. Automatic Story Generation: Challenges and Attempts
@The_Amool @beckypeng6 @Sylvia_Sparkle @sarahwiegreffe

A survey of the field of automated story generation and what challenges still lay ahead of us despite nearly 50 years of trying.
Here is the correct arxiv link for #2:

My apologies to @BlancheMinerva who was also an author.

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More from @mark_riedl

15 May 20
I’m finally ready to release my neural net based lyrics parody generation system…

Introducing: Weird A.I. Yankovic!

Runs on Google Colab:…
You can provide the rhyme scheme and syllables per line for an existing song, and it will write new lyrics to match.

In the true spirit of parody, here is a Michael Jackson song (“Beat It”) rewritten to be about food.

Then you can sing the song yourself to the horror of others
With a little bit of extra work (you provide a mp3 or mp4), the system will produce a karaoke video to make it easier to sing along
Read 10 tweets
21 Sep 19
Hollywood: It’s about an experimental AI that—

Me: that fails to converge until the programmer cleans up millions of lines of labeled data? Right! Very suspenseful! Never know if that’s going to work.

Hollywood: you’re fired
Hollywood: the AI has a robot body that—

Me: can’t pick up any objects unless they a placed in a very specific way on a table at just the right height. The struggle is real.

Hollywood: No—
Read 7 tweets
19 Feb 19
This is a problem for the DOD and has been for a long time…

But it is addressable by fixing our higher education system, which is not producing enough AI/ML engineers and skewed toward a small # of elite universities.
Under the belief that there are no secret algorithms, only secret engineering, the DOD mostly needs people that can build hardened AI/ML systems (let’s ignore the question of what these systems are for for the moment).
Right now there is high demand in industry for these skills. The demand is high because when it comes to AI and ML, our education system is skewed toward a small number of elite universities that have the resources to do research and thus train graduate students in these areas.
Read 7 tweets
17 Sep 18
I’m super geeked ➡️ this is video of @MatthewGuz playing a game generated by a ML algorithm trained on video of Super Mario Bros., Kirby, and Mega Man Everything is learned from scratch: level design and mechanics/rules.

What I like about this video is that the game is very different from any of the training examples.

The conceptual expansion algorithm is able to extrapolate beyond the training data, hypothesizing the existence of models that aren’t directly supported by the training data
.@MatthewGuz and I think the ability to extrapolate beyond training data is a very important quality for ML-based computational creativity.

We think conceptual expansion may be potentially important for ML generalization and domain transfer too.
Read 4 tweets

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