Gaiku

Yael Kinor

Generating Haiku with Word Association NormsArticle by Yael Netzer, David Gabay, Yoav Goldberg, Michael Elhadad

Contents- Computational Creativity

- What is Haiku?

- Where to start?

- Algorithm

- Experiment

Computational Creativity Model, simulate or replicate creativity using a computer

- Program capable of human-level creativity

- Better understand human creativity

- Enhance human creativity

Computational Creativity Challenge -

Understanding and modeling vague knowledge (beautiful, touching, funny)

Oulipo Ouvroir de littérature potentielle - “Workshop of Potential Literature"

- French writers and mathematicians- Founded in 1960- Create works using constraints

“seeking of new structures and patterns which may be used by writers in any way they enjoy."

Oulipo Methods

- N+7- Lipogram- Combinatorics- Snowball

Poetry Generation

Three properties -

- Meaningfulness

- Grammaticality

- Poeticness

How can we apply to Haiku?

Haiku俳句

Short form of Japanese poetry from 16th century

- Present tense, no judgment words

- Beauty lies in the things left unsaid

- Read between the lines

- Minimalism

- Express anything and everything

Haiku俳句

Follows three technical rules -

- Kireji = cutting word

- 17 on = syllables

- Kigo = seasonal reference

Kireji Special category of words

- Sense of closure or pause

- Emotional flavour and emphasis

- Ka, kana, -keri, -ramu, -shi, -tsu, ya

- No exact equivalent in English (dash, ellipsis)

On Phonology unit that determines syllable weight (μ)

- A long vowel is one syllable but two moras

(Aa, Ou, Ei)

- 17 moras - 3 lines of 5 / 7 / 5

We will talk about syllables

Kigo A word or phrase associated with a particular season

- Categories - seasons, plants, animals, earth,

humanity, heavens

- Early/Mid/Late of Spring/Summer/Autumn/Winter

- Saijiki

古池や蛙飛びこむ水の音　

furu ike ya / kawazu tobi komu / mizu no oto

an old ponda frog jumps intothe sound of water

Basho - 17 cen.

Haiku俳句 - Kireji = cutting word

- 17 on = syllables

- Kigo = seasonal reference

Haiku俳句 To convey one's mood In seventeen

syllables Is very diffic[Haiku, defined by John Cooper Clarke]

We will use a less constraining definition..

How to write a computer program to generate Haiku?

Let’s ask Stack Overflow

We Need Words

Lexical databases

- WordNet - Thesaurus

Predictable and limited..

We Need Words

Human mind uses varied associations

- Paradigmatic - (doctor-nurse)

- Syntagmatic - (mash-potato)

- Emotional - (math-yuck)

We Need Words

Creativity requires -

- Knowledge

- Imagination

- Filtering

Bad Examples -

Haiku generator 1

Haiku Generator 2

WANs Word Association Norms

- 40 years of psychological research

- Typical associations evoked by trigger

- Stable across people, time, language

Excellent for Haiku!

University of South Florida Free Association Norms

- 5K cue words + 10K additional words

Are Haiku more associative than news or prose?

WANs

Modelling

WordNet

WANs

Modelling

WAN graph

WANs Basic definitions -

Shortest path distances -

asso_dist(w1, w2) , wordNet_dist(w1, w2)

If [ asso_dist(w1, w2) < 3 ] => asso_related(w1, w2) = 1If [ wordNet_dist(w1, w2) < 4 ] => wordNet_related(w1, w2) = 1

If [ w1, w2 in WAN ] => in_wan(w1, w2) = 1

WANs

WANs

Associativity Test

- 200 Haiku- 12 word sequences Project Gutenberg- 12 word sequences NANC newswire corpus ($$)

Generating Haiku using WANs

5 stages of creative process -

- Theme selection

- Syntactic planning

- Content selection / semantic planning

- Filtered over-generation

- Re-ranking

Algorithm

Dataset

- USF WAN - 15K words

- Haiku corpus - 3500 English Haikus

- Google N-grams - 1TB of data

- Project Gutenberg - Easier to POS tag

Algorithm

Theme Selection

1. User supplied seed word

2. Consult WAN database -

● 8 x Random walks from seed, 3 steps

● Keep all resulting words

Theme selection

Random walks -

association graph vs. WordNet graph

Algorithm

Syntactic Planning

Training stage -

1. POS-tag each Haiku

2. Extract line pattern - “DT_the JJ NN”

3. Top 40 frequent line patterns

=> Total 120 patterns

Algorithm

Syntactic Planning

Generate Haiku -

- Choose random pattern for 1st line

- Choose 2nd line

- Choose 3rd line

Algorithm

Syntactic Planning

Result - 3 line Haiku skeleton -

- Num of words in each line

- POS tags of words

- Placement of function words

Algorithm

Content Selection

For each Haiku line -

- Search match in datasets (Google, Project Gutenberg)

- Syntactic patterns

- 1st line to contain seed word

- 2nd and 3rd contain any theme word

Algorithm

Syntactic Planning

NN NN

DET_a NN of NNS

PP_in DET_the NN

alligator peara handful of whitesin the spring

avocado peara kind of bootsin the fall

pear salada season of tearsin the summer

pear treea seasoning of spicesin the fall

Algorithm

Over Generation

- Create all possible Haiku candidates!- Filter away Haikus with undesired properties

Algorithm

Re-ranking

Heuristics -

Maximize number of 2nd degree associations

*This method chooses quantity over quality..

Is computer generated poem good enough?

fishing guidesboat in the backgrounda new trip

blossomlessbut not unlovedthe old magnolia

first date —the little pileof anchovies

a holy cowa carton of milkseeking a church

blind snakeson the wet grasstombstoned terror

iced over pondI skip a rockthe entire width

Experiment Turing Test

Test of machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.

Experiment First set (AUTO)

- 10 random Haikus from corpus- 15 computer generated Haikus -

- Human identify main words in first line- Use main word as seed - 2 main words => 2 haikus

Second set (SEL)

- 9 award-winning Haikus - 17 human-selected computer generated Haikus

Experiment - 50 adult subjects

- “Human” / “Not Human”

- 1-5 grade

Experiment

Results

(SEL) 66.7% correct (AUTO) 61.4% correct

Experiment

AUTOResults

Cherry tree / poisonous flowers lie / blooming (72.2% human)

Spring bloom / showing / the sun’s pyre(63.8% human)

Experiment

SEL Results

Early dew / the water contains / teaspoons of honey (77.2% human)

Space journey / musical instruments mythology / of similar drugs(9% human)

Can a computer generated creation capture the beauty of

a flower?

Future work

- Enrich information in WordNet

- New creative structures

- Assisting with SLI

fishing guidesboat in the backgrounda new trip

blossomlessbut not unlovedthe old magnolia

first date —the little pileof anchovies

a holy cowa carton of milkseeking a church

blind snakeson the wet grasstombstoned terror

iced over pondI skip a rockthe entire width