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creativity / a pleasing field / of bloom: Generating Haiku Poems. Read all about it: Gaiku: Generating Haiku with Word Association Norms Yael Netzer, David Gabay , Yoav Goldberg and Michael Elhadad. Yoav Goldberg. ISI NL Seminar. Motivation. Why work on poetry generation?.

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creativity a pleasing field of bloom generating haiku poems

creativity / a pleasing field / of bloom:Generating Haiku Poems

Read all about it:

Gaiku: Generating Haiku with Word Association Norms

Yael Netzer, David Gabay , Yoav Goldberg and Michael Elhadad

Yoav Goldberg

ISI NL Seminar

motivation

Motivation

Why work on poetry generation?

motivation1
Motivation
  • It’s cool
  • Seem like a fun thing to do

We like doing fun stuff and being cool

(also, nice that wife is actually interested in what you do for a couple of weeks)

motivation2
Motivation
  • It’s cool
  • Seem like a fun thing to do

We like doing fun stuff and being cool

(also, nice that wife is actually interested in what you do for a couple of weeks)

You can have fun and do cool stuff too!

give computational creativity a shot

key ideas constraints
Key Ideas - constraints

Form constraints aid creativity

  • help focus on content
  • force interesting solutions
key ideas associativeness
Key Ideas - associativeness

Associations are central to human thinking

Associations are at the core of creativity

Associations are key to poetry perception

Many associative layers can be active simultaneously and contribute to meaning

slide7
People like to interpret / fill the gaps

 Poetry reading is also a creative process

haiku1
Haiku
  • Form of poetry
  • Originated in Japan, 16th Century
  • Three lines of 5,7,5 phonetic units (mora)
  • Use present tense and use no judgmental words
  • Adopted in Western languages, 20th Century
  • Basho Haiku
  • 古池や蛙飛込む水の音
        • old pond . . .
        • a frog leaps in
        • water’s sound
amarican haiku
Amarican Haiku

“Mainly I've been back to my books and writings and being nice and quiet and lazy.”

amarican haiku1
Amarican Haiku

The American Haiku is not exactly the Japanese Haiku. The Japanese Haiku is strictly disciplined to seventeen syllables but since the language structure is different I don't think American Haikus (short three-line poems intended to be completely packed with Void of Whole) should worry about syllables because American speech is something again...bursting to pop.   Above all, a Haiku must be very simple and free of all poetic trickery and make a little picture and yet be as airy and graceful as a Vivaldi Pastorella."

slide13
old pond . . .

a frog leaps in

water’s sound

old pond

frog leaping

splash

pond

frog

plop!

slide14
picnic

in the sun-dappled courtyard

my freckled banana

my father and I

paint the barn

compare wars

slide15
fishing guides

boat in the background

a new trip

iced over pond

I skip a rock

the entire width

a holy cow

a carton of milk

seeking a church

blind snakeson the wet grasstombstoned terror

Holding up my

purring cat to the moon

I sighed

blossomless

but not unloved

the old magnolia

first date —

the little pile

of anchovies

slide17
Bo

y

S

ul

slide18
Bo

y

S

ul

Structure

slide19
Bo

y

S

ul

Content

Structure

slide20
Bo

y

S

ul

Inspiring,

Interesting,

Intriguing,

Joyful,

3 lines,

Grammatical,

Haiku-like

previous works
Previous works
  • Manurung [2003]
  • Manurung et al. [2000]
  • Gervas [2001]
  • Knight [2010]  future previous

Emphasize on Structure, less on Content

data driven
Data Driven
  • Haiku Corpus
    • ~3,500 Haiku in English
    • Various sources
      • amateurish sites
      • children’s writings
      • translations of classic Japanese Haiku of Bashu and others
      • ’official’ sites of Haiku Associations (e.g., Haiku Path - Haiku Society of America).
pattern extraction
Pattern Extraction

Line 1 Patterns:

280 JJ NN276 NN NN...

Line 2 Patterns:

64 DT_the JJ NN

Line 3 Patterns:

….

NN IN_of NNPDT_a NN IN_ofNNS

NN NNNNS CC NNSIN_on DT_a NN NN

POS Tag

Count

Count

Pattern Transitions:

P(line2==DT_the NN | line1==JJ NN) = ...

generation
Generation

Google 1T-Web / Proj Gutenberg

Line 1 Patterns:

280 JJ NN276 NN NN...

Line 2 Patterns:

64 DT_the JJ NN

Line 3 Patterns:

….

POS Tagged

Pattern Transitions:

P(line2==DT_the NN |

line1==JJ NN) = ...

generation1
Generation

Google 1T-Web / Proj Gutenberg

Line 1 Patterns:

280 JJ NN276 NN NN...

Line 2 Patterns:

64 DT_the JJ NN

Line 3 Patterns:

….

POS Tagged

Pattern Transitions:

P(line2==DT_the NN |

line1==JJ NN) = ...

JJ NNSDT_a JJ NNIN_of NN

generation2
Generation

Google 1T-Web / Proj Gutenberg

Line 1 Patterns:

280 JJ NN276 NN NN...

Line 2 Patterns:

64 DT_the JJ NN

Line 3 Patterns:

….

POS Tagged

match

Pattern Transitions:

P(line2==DT_the NN |

line1==JJ NN) = ...

JJ NNSDT_a JJ NNIN_of NN

generation3
Generation

Google 1T-Web / Proj Gutenberg

Line 1 Patterns:

280 JJ NN276 NN NN...

Line 2 Patterns:

64 DT_the JJ NN

Line 3 Patterns:

….

POS Tagged

match

Pattern Transitions:

P(line2==DT_the NN |

line1==JJ NN) = ...

pouring catsa pilot careof fighter

JJ NNSDT_a JJ NNIN_of NN

generation4
Generation

Google 1T-Web / Proj Gutenberg

Line 1 Patterns:

AA BB CC / 12

BB CC DD / 10

Line 2 Patterns:

CC DD EE / 20

Line 3 Patterns:

….

Grammatical output

Preserves Haiku “Texture”

POS Tagged

match

Pattern Transitions:

P(Line2=AA BB | Line1= XX YY)

pouring catsa pilot careof fighter

JJ NNSDT_a JJ NNIN_of NN

generation5
Generation

Google 1T-Web / Proj Gutenberg

Line 1 Patterns:

AA BB CC / 12

BB CC DD / 10

Line 2 Patterns:

CC DD EE / 20

Line 3 Patterns:

….

Grammatical output

Preserves Haiku “Texture”

POS Tagged

Not a great story, though.

match

Pattern Transitions:

P(Line2=AA BB | Line1= XX YY)

pouring catsa pilot careof fighter

JJ NNSDT_a JJ NNIN_of NN

slide32
Soul?
  • Requirements: good “story”
    • cohesive
    • surprising
    • provoke feelings/emotions
    • metaphorical
    • “Should leave the reader wondering…”

… Creative!

slide33
Soul?
  • An idea:

capture “story” seed as sequence of concepts

butterfly, spring, flower

thief , steal , jail

mosquito, blood, vampire

but not any seed will do

cat , feline , claw  too cohesive

computer , coat , queen too divergent

slide34
Soul?

Is WordNet a good soul?

not really

it may give cohesiveness,

but bad stories

butterfly spring flower
Butterfly Spring Flower
  • The connection between these words is reconstructable by human
  • It is not available in WordNet
  • Where can we find such relations?
word association norms wan
Word Association Norms (WAN)
  • Collection of cue words a set of free associations (targets) with quantitative and statistical measures.

(mouse CAT 0.5, RAT 0.08, CHEESE 0.07, HOLE 0.05…)

  • Given a cue - collect immediate responses of first word that comes to mind.
  • Largest WAN we know for English is the University of South Florida Free Association Norms (Nelson et al., 1998).

http://w3.usf.edu/FreeAssociation/

  • 5,019 cue words and 10,469 additional target that were collected with more than 6,000 participants since 1973.

WAN – weighted directed graph, nodes are stemmed words.

slide39
water

spring

water

fall

fall

flower

butterfly

green

bloom

why word associations
Why Word Associations
  • Added value of WAN: an insight on language, not found in WordNet or are hard to acquire from corpora [Sinopalnikova & Smrz 2004]
  • Associative thinking takes part in the process of writing and reading poetry
  • Haiku, because so short - relies on lexical associations for concept progression

Hypothesis: word-associations are good catalyzers for creativity, can be used as a building block in the creative process of Haiku generation.

filling body with soul theme selection
Filling body with soul: Theme Selection
  • Generating the seed of the story:
    • Start with a word
  • random walk on a word graph

Many possible variants.

We currently use:

start with the node of the seed word

do several short random walks

keep resulting word set

filling body with soul
Filling body with soul

Input: seed word

  • generate structural skeleton
  • perform several short random walks on Assoc. graph, creating an association set
  • choose first line containing seed word
  • choose other lines containing a word from the set

This is adequate, but relations might be too straightforward

searching for a better soul
Searching for a better soul
  • Generate several poems for the pattern, then select the best one

(people do that too: try out various ideas, write, rewrite, throw away, chose best one)

searching for a better soul1
Searching for a better soul
  • Generate several poems for the pattern, then select the best one

(people do that too: try out various ideas, write, rewrite, throw away, chose best one)

We rank haikus based on associativity measure

This ranking catches further “residual” relations

education plays a role too
Education plays a role too..

At first we used Google web n-grams

SILVER:

golden age of animation

saves a lot of money

with fish

education plays a role too1
Education plays a role too..

At first we used Google web n-grams

SILVER:

golden age of animation

saves a lot of money

with fish

ANIMAL:

animal nature

wild italian housewives

on a happy face

education plays a role too2
Education plays a role too..

At first we used Google web n-grams

SILVER:

golden age of animation

saves a lot of money

with fish

FREE:

the sample solution

free nude adult webcams

on a statutory holiday

ANIMAL:

animal nature

wild italian housewives

on a happy face

education plays a role too3
Education plays a role too..

At first we used Google web n-grams

SILVER:

golden age of animation

saves a lot of money

with fish

FREE:

the sample solution

free nude adult webcams

on a statutory holiday

ANIMAL:

animal nature

wild italian housewives

on a happy face

CUTE:

cute college girls

young pussy cum

hardcore horse zoophilia

then we decided to switch to Gutenberg project

turing test
“Turing Test”
  • Was this Haiku written by human or computer?
  • How would you rate it between 1 to 5?
  • Settings:
    • AUTO: 15 Haiku created by Gaiku without any manual selection, 10 random human Haiku (same seed words)
    • SEL: 17 Haiku created by Gaiku, selected manually out of several runs, 9 award winning human Haiku
  • 52 subjects
interesting observations
Interesting observations
  • Every subject was wrong at least 3 times
    • at least once in each direction
  • CS students “better” at recognizing computer poems than Foreign Literature Students
  • Foreign Literature students assign higher scores in general
some subjects comments
Some subjects comments:
  • On computer generated:

“this is too stupid to be written by a computer”

  • On human generated:

“this metaphor is too trivial to be written by a human”

thoughts on turing tests
Thoughts on Turing tests
  • People are not very good at judging what a computer can or can’t do
  • Who is under test in a Turing test?
    • The computer program?
    • The human haiku writers?
    • The human participating in the test?
regardless of evaluation limitations
Regardless of evaluation limitations

I feel Gaiku is indeed a reasonable Haiku poet

It is certainly better than me at writing Haiku poems

I find myself constantly amazed by some of its outputs

I find many of them to be intriguing / thought-provoking / amusing / fascinating.

I can definitely recognize Gaiku outputs from Human poems. But maybe it just means it has a distinct style?

the best of gaiku
The Best of Gaiku

early dew

the water contains

teaspoons of honey

  • Best in SEL. Classified as human - 77.2%, average grade 3.09
  • Best in AUTO. Classified as human - 72.2%, average grade 2.75

cherry tree

poisonous flowers lie

blooming

slide59
fishing guides

boat in the background

a new trip

iced over pond

I skip a rock

the entire width

a holy cow

a carton of milk

seeking a church

blind snakeson the wet grasstombstoned terror

Holding up my

purring cat to the moon

I sighed

blossomless

but not unloved

the old magnolia

first date —

the little pile

of anchovies

slide60
fishing guides

boat in the background

a new trip

iced over pond

I skip a rock

the entire width

a holy cow

a carton of milk

seeking a church

blind snakeson the wet grasstombstoned terror

Holding up my

purring cat to the moon

I sighed

blossomless

but not unloved

the old magnolia

first date —

the little pile

of anchovies

slide61
fishing guides

boat in the background

a new trip

iced over pond

I skip a rock

the entire width

a holy cow

a carton of milk

seeking a church

blind snakeson the wet grasstombstoned terror

Holding up my

purring cat to the moon

I sighed

blossomless

but not unloved

the old magnolia

first date —

the little pile

of anchovies

interesting stuff to be explored
Interesting stuff to be explored
  • Better modeling of association progression
    • Can we model Surprise? Suspense?
    • Higher-order associations
      • Can we create association priming?
      • Can we create association-garden-pathing?
  • There is more to haikus than associations
  • Can we generate based on a theme, not just a seed word?
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