1. Templates in Linguistics: Why
Garbage Garbage?
Presented by:
Hussein Ghaly

2. 1- Garbage Disposal
Yaseen Ghaly, 3 Years Old:
• Papy laih zebala zebala? (Dad, why
garbage garbage?) > Why are you
carrying two garbage bags?
• Papy laih bang bang? (Dad, why bang
bang?) > Why are you making this “bang
bang/hammering” sound?

3. 2- Making a Template
• Yaseen seems to be using this template:
• “Papy, laih X?” (Dad, why X)
• Where X can be anything:
– Garbage Garbage
– Bang Bang
– Sleeping
– …
– 天空是绿色的 (foreign word/code switching)

4. 3- To build a language
• How can the linguistic expression from
simple sentences into language such as
ours?
• Answer: Recursion

5. 4- Recursively
• An example of
recursion found
by Salma Ghaly,
6 Years old.

6. Main Claim
• Language is built using simple (idiomatic)
templates. The complexity comes from
recursion.

7. Outline
• Starting Assumptions
• Learning templates (Language Acquisition)
• Cross Linguistic Template Linearity
• Selecting A template (Semantic-Pragmatic
Prompt)
• Extending A template (Template Malleability)
• Applications of Templates (Information
Extraction and Machine Translation)

8. Starting Assumptions - Syntax
• In the syntax literature, language is a
lexicon of words, and a computational
system to put these words where they
should form a grammatical sentence.
Lexicon
Computation
System

9. Starting Assumptions – Templates
Framework
• The “lexicon”, which is stored in the memory, is extended
with a list of templates, also stored in memory.
• The computational system only manages what to fill the
placeholders within templates.
Word Lexicon Computation
System
Template Lexicon

10. Learning Templates
• The “garbage gabage” example indicates:
– A child can intuitively form a template for
plurals (that is applicable in some human
languages such as Bhasa Malaysia (e.g.
kanak kanak=children)
– A child can put anything in the placeholder X
within the sentence template “Dad, Why X?”
• But these hypotheses would need further
evidence from First Language Acquistion

11. Template Linearity
• English
– I love you.
– I miss you.
– I need you.
• French
– Je t’aime.
– Tu me manques.
– J’ai besoin de toi.
Clearly, the linear order is very different between Constructions in
different languages.
This should entice us to think about how these constructions are
generated.

12. Semantic-Pragmatic Prompt
• An area of overlap between the reason, context,
and information content of some sentence.
• Start with list of arguments (X1: I, X2: You)
• I Want to express [+feeling] [+positive]
[+distance], therefore:
– in English, we invoke the template I miss X2.
– In French, we invoke the template X2 me manques
(with some adjustments depending on pronouns, etc)
• So I can utter the sentence after filling the
template:
– I miss Randa.
– Randa me manque.

13. Template Variability
• Almost everything can be said in an alternative
way:
– Godzilla destroyed the City, which is unfortunate.
– It is unfortunate that Godzilla destroyed the city.
– The destruction of the city by Godzilla is unfortunate.
• So, there are different templates to express the
relation between these four entities (being
unfortunate, the destruction, Godzilla, the City).
This again feeds into the argument of non-
linearity of templates, this time within the same
language.

14. Template Malleability
• Meaning how easy the template can be re-
shaped. This includes the following:
– Tense malleability:
• John was eating fish.
• John has been eating fish.
– Synonym malleability:
• Sarah cannot tolerate this any more.
• Sarah cannot put up with this anymore.
• The idea of malleability enables us to avoid
accounting for hundreds of millions of
combinations of basic templates.

15. Using Templates
• For information Extraction (e.g. Banko and
Etzioni 2008), where templates where
used to extract (is-a) relationships
between entities.

16. Using Templates in Machine
Translation
• Was first suggested by (Nagao, 1984)
under the name of Example-Based
Machine Translation. He also indicted this
approach is relevant to Second Language
Acquisition.

17. Using Templates in Machine
Translation
• Current state of the
art Phrase-Based
Statistical Machine
Translation
techniques uses
contiguous chunks.
(Koehn, 2010)

18. Using Templates in Machine
Translation
• But using contiguous chunks misses many
phrases where there is a difference in
word order between the two languages.
- needs a lot of training data 
• To compensate for this, a statistical
reordering model is used
- can make the output unintelligible 

19. Using Templates in Machine
Translation
Chunk:
Michael assumes that he will stay in the house ->
Michael geht davon aus, dass er im haus bleibt
Subchunks:
Michael -> Michael
in the house -> im haus
So by removing (stenciling) subcunks from the chunk we get a translation template
X1 assumes that he will stay X2 ->
X1 geht davon aus, dass er X2 bleibt
- preserves word order 
- can apply to many sentences not seen before 
- requires less training data 
- can set restrictions on the type of placeholders (X1: NP , X2: PP) 

20. •Thank X1!
(X1 = You )