GitHub/gf-core
1
0
Fork 1
mirror of https://github.com/GrammaticalFramework/gf-core.git synced 2026-05-22 09:32:53 -06:00
Code Issues Packages Projects Releases Wiki Activity

dep types in new tutorial

Browse Source
This commit is contained in:
aarne
2006-06-16 08:35:24 +00:00
parent 6a0dd96623
commit 3488c5839e
2 changed files with 1762 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1026
doc/tutorial/gf-tutorial2.html
View File

File diff suppressed because it is too large Load Diff

786
doc/tutorial/gf-tutorial2.txt
View File

@@ -1894,14 +1894,796 @@ library.
===Dependent types=== ===Dependent types===
===Higher-order abstract syntax=== **Dependent types** are a characteristic feature of GF,
inherited from the
**constructive type theory** of Martin-Löf and
distinguishing GF from most other grammar formalisms and
functional programming languages.
The initial main motivation for developing GF was, indeed,
to have a grammar formalism with dependent types.
As can be inferred from the fact that we introduce them only now,
after having written lots of grammars without them,
dependent types are no longer the only motivation for GF.
But they are still important and interesting.
Dependent types can be used for stating stronger
**conditions of well-formedness** than non-dependent types.
A simple example is postal addresses. Ignoring the other details,
let us take a look at addresses consisting of
a street, a city, and a country.
```
abstract Address = {
cat
Address ; Country ; City ; Street ;
fun
mkAddress : Country -> City -> Street -> Address ;
UK, France : Country ;
Paris, London, Grenoble : City ;
OxfordSt, ShaftesburyAve, BdRaspail, RueBlondel, AvAlsaceLorraine : Street ;
}
```
The linearization rules
are straightforward,
```
lin
mkAddress country city street = ss (street ++ "," ++ city ++ "," ++ country) ;
UK = ss ("U.K.") ;
France = ss ("France") ;
Paris = ss ("Paris") ;
London = ss ("London") ;
Grenoble = ss ("Grenoble") ;
OxfordSt = ss ("Oxford" ++ "Street") ;
ShaftesburyAve = ss ("Shaftesbury" ++ "Avenue") ;
BdRaspail = ss ("boulevard" ++ "Raspail") ;
RueBlondel = ss ("rue" ++ "Blondel") ;
AvAlsaceLorraine = ss ("avenue" ++ "Alsace-Lorraine") ;
```
with the exception of ``mkAddress``, where we have
reversed the order of the constituents. The type of ``mkAddress``
in the abstract syntax takes its arguments in a "logical" order,
with increasing precision. (This order is sometimes even used in the concrete
syntax of addresses, e.g. in Russia).
Both existing and non-existing addresses are recognized by this
grammar. The non-existing ones in the following randomly generated
list have afterwards been marked by *:
```
> gr -cat=Address -number=7 | l
* Oxford Street , Paris , France
* Shaftesbury Avenue , Grenoble , U.K.
boulevard Raspail , Paris , France
* rue Blondel , Grenoble , U.K.
* Shaftesbury Avenue , Grenoble , France
* Oxford Street , London , France
* Shaftesbury Avenue , Grenoble , France
```
Dependent types provide a way to guarantee that addresses are
well-formed. What we do is to include **contexts** in
``cat`` judgements:
```
cat Address ;
cat Country ;
cat City Country ;
cat Street (x : Country)(y : City x) ;
```
The first two judgements are as before, but the third one makes
``City`` dependent on ``Country``: there are no longer just cities,
but cities of the U.K. and cities of France. The fourth judgement
makes ``Street`` dependent on ``City``; but since
``City`` is itself dependent on ``Country``, we must
include them both in the context, moreover guaranteeing that
the city is one of the given country. Since the context itself
is built by using a dependent type, we have to use variables
to indicate the dependencies. The judgement we used for ``City``
is actually shorthand for
```
cat City (x : Country)
```
which is only possible if the subsequent context does not depend on ``x``.
The ``fun`` judgements of the grammar are modified accordingly:
```
fun
mkAddress : (x : Country) -> (y : City x) -> Street x y -> Address ;
UK : Country ;
France : Country ;
Paris : City France ;
London : City UK ;
Grenoble : City France ;
OxfordSt : Street UK London ;
ShaftesburyAve : Street UK London ;
BdRaspail : Street France Paris ;
RueBlondel : Street France Paris ;
AvAlsaceLorraine : Street France Grenoble ;
```
Since the type of ``mkAddress`` now has dependencies among
its argument types, we have to use variables just like we used in
the context of ``Street`` above. What we claimed to be the
"logical" order of the arguments is now forced by the type system
of GF: a variable must be declared (=bound) before it can be
referenced (=used).
The effect of dependent types is that the *-marked addresses above are
no longer well-formed. What the GF parser actually does is that it
initially accepts them (by using a context-free parsing algorithm)
and then rejects them (by type checking). The random generator does not produce
illegal addresses (this could be useful in bulk mailing!).
The linearization algorithm does not care about type dependencies;
actually, since the //categories// (ignoring their arguments)
are the same in both abstract syntaxes,
we use the same concrete syntax
for both of them.
**Remark**. Function types //without//
variables are actually a shorthand notation: writing
```
fun PredV1 : NP -> V1 -> S
```
is shorthand for
```
fun PredV1 : (x : NP) -> (y : V1) -> S
```
or any other naming of the variables. Actually the use of variables
sometimes shortens the code, since we can write e.g.
```
fun ConjNP : Conj -> (x,y : NP) -> NP ;
oper triple : (x,y,z : Str) -> Str = \x,y,z -> x ++ y ++ z ;
```
===Dependent types in syntax editing===
An extra advantage of dependent types is seen in
syntax editing:
when menus with possible refinements are created,
only those functions are shown that are type-correct.
For instance, if the editor state is
```
mkAddress : Address
UK : Country
* ?2 : City UK
?3 : Street UK ?2
```
only the cities of the U.K. are shown in the city menu.
What is more, editing in the state
```
mkAddress : Address
?1 : Country
?2 : City (?1)
* ?3 : Street (?1) (?2)
```
//starts// from the ``Street`` argument,
which enables GF automatically to infer the city and the country.
Thus, in addition to guaranteeing the meaningfulness of the results,
dependent types can shorten editing sessions considerably.
===Dependent types in concrete syntax===
The **functional fragment** of GF
terms and types comprises function types, applications, lambda
abstracts, constants, and variables. This fragment is similar in
abstract and concrete syntax. In particular,
dependent types are also available in concrete syntax.
We have not made use of them yet,
but we will now look at one example of how they
can be used.
Those readers who are familiar with functional programming languages
like ML and Haskell, may already have missed **polymorphic**
functions. For instance, Haskell programmers have access to
the functions
```
const :: a -> b -> a
const c x = c
flip :: (a -> b ->c) -> b -> a -> c
flip f y x = f x y
```
which can be used for any given types ``a``,``b``, and ``c``.
The GF counterpart of polymorphic functions are **monomorphic**
functions with explicit **type variables**. Thus the above
definitions can be written
```
oper const :(a,b : Type) -> a -> b -> a =
\_,_,c,x -> c ;
oper flip : (a,b,c : Type) -> (a -> b ->c) -> b -> a -> c =
\_,_,_,f,x,y -> f y x ;
```
When the operations are used, the type checker requires
them to be equipped with all their arguments; this may be a nuisance
for the Haskell or ML programmer.
===Expressing selectional restrictions===
This section introduces a way of using dependent types to
formalize a notion known as **selectional restrictions**
in linguistics. We first present a mathematical model
of the notion, and then integrate it in a linguistically
motivated syntax.
In linguistics, a
grammar is usually thought of as being about **syntactic well-formedness**
in a rather liberal sense: an expression can be well-formed without
being meaningful, in other words, without being
**semantically well-formed**.
For instance, the sentence
```
the number 2 is equilateral
```
is syntactically well-formed but semantically ill-formed.
It is well-formed because it combines a well-formed
noun phrase ("the number 2") with a well-formed
verb phrase ("is equilateral") in accordance with the
rule that the verb phrase is inflected in the
number of the noun phrase:
```
fun PredV1 : NP -> V1 -> S ;
lin PredV1 np v1 = {s = np.s ++ v1.s ! np.n} ;
```
It is ill-formed because the predicate "is equilateral"
is only defined for triangles, not for numbers.
In a straightforward type-theoretical formalization of
mathematics, domains of mathematical objects
are defined as types. In GF, we could write
```
cat Nat ;
cat Triangle ;
cat Prop ;
```
for the types of natural numbers, triangles, and propositions,
respectively.
Noun phrases are typed as objects of basic types other than
``Prop``, whereas verb phrases are functions from basic types
to ``Prop``. For instance,
```
fun two : Nat ;
fun Even : Nat -> Prop ;
fun Equilateral : Triangle -> Prop ;
```
With these judgements, and the linearization rules
```
lin two = ss ["the number 2"] ;
lin Even x = ss (x.s ++ ["is even"]) ;
lin Equilateral x = ss (x.s ++ ["is equilateral"]) ;
```
we can form the proposition ``Even two``
```
the number 2 is even
```
but no proposition linearized to
```
the number 2 is equilateral
```
since ``Equilateral two`` is not a well-formed type-theoretical object.
When formalizing mathematics, e.g. in the purpose of
computer-assisted theorem proving, we are certainly interested
in semantic well-formedness: we want to be sure that a proposition makes
sense before we make the effort of proving it. The straightforward typing
of nouns and predicates shown above is the way in which this
is guaranteed in various proof systems based on type theory.
(Notice that it is still possible to form //false// propositions,
e.g. "the number 3 is even".
False and meaningless are different things.)
As shown by the linearization rules for ``two``, ``Even``,
etc, it //is// possible to use straightforward mathematical typings
as the abstract syntax of a grammar. However, this syntax is not very
expressive linguistically: for instance, there is no distinction between
adjectives and verbs. It is hard to give rules for structures like
adjectival modification ("even number") and conjunction of predicates
("even or odd").
By using dependent types, it is simple to combine a linguistically
motivated system of categories with mathematically motivated
type restrictions. What we need is a category of domains of
individual objects,
```
cat Dom
```
and dependencies of other categories on this:
```
cat
S ; -- sentence
V1 Dom ; -- one-place verb
V2 Dom Dom ; -- two-place verb
A1 Dom ; -- one-place adjective
A2 Dom Dom ; -- two-place adjective
PN Dom ; -- proper name
NP Dom ; -- noun phrase
Conj ; -- conjunction
Det ; -- determiner
```
The number of ``Dom`` arguments depends on the semantic type
corresponding to the category: one-place verbs and adjectives
correspond to types of the form
```
A -> Prop
```
whereas two-place verbs and adjectives correspond to types of the form
```
A -> B -> Prop
```
where the domains ``A`` and ``B`` can be distinct.
Proper names correspond to types of the form
```
A
```
that is, individual objects of the domain ``A``. Noun phrases
correspond to
```
(A -> Prop) -> Prop
```
that is, **quantifiers** over the domain ``A``.
Sentences, conjunctions, and determiners correspond to
```
Prop
Prop -> Prop -> Prop
(A : Dom) -> (A -> Prop) -> Prop
```
respectively,
and are thus independent of domain. As for common nouns ``CN``,
the simplest semantics is that they correspond to
```
Dom
```
In this section, we will, in fact, write ``Dom`` instead of ``CN``.
Having thus parametrized categories on domains, we have to reformulate
the rules of predication, etc, accordingly. This is straightforward:
```
fun
PredV1 : (A : Dom) -> NP A -> V1 A -> S ;
ComplV2 : (A,B : Dom) -> V2 A B -> NP B -> V1 A ;
UsePN : (A : Dom) -> PN A -> NP A ;
DetCN : Det -> (A : Dom) -> NP A ;
ModA1 : (A : Dom) -> A1 A -> Dom ;
ConjS : Conj -> S -> S -> S ;
ConjV1 : (A : Dom) -> Conj -> V1 A -> V1 A -> V1 A ;
```
In linearization rules,
we typically use wildcards for the domain arguments,
to get arities right:
```
lin
PredV1 _ np vp = ss (np.s ++ vp.s) ;
ComplV2 _ _ v2 np = ss (v2.s ++ np.s) ;
UsePN _ pn = pn ;
DetCN det cn = ss (det.s ++ cn.s) ;
ModA1 cn a1 = ss (a1.s ++ cn.s) ;
ConjS conj s1 s2 = ss (s1.s ++ conj.s ++ s2.s) ;
ConjV1 _ conj v1 v2 = ss (v1.s ++ conj.s ++ v2.s) ;
```
The domain arguments thus get suppressed in linearization.
Parsing initially returns metavariables for them,
but type checking can usually restore them
by inference from those arguments that are not suppressed.
One traditional linguistic example of domain restrictions
(= selectional restrictions) is the contrast between the two sentences
```
John plays golf
golf plays John
```
To explain the contrast, we introduce the functions
```
human : Dom ;
game : Dom ;
play : V2 human game ;
John : PN human ;
Golf : PN game ;
```
Both sentences still pass the context-free parser,
returning trees with lots of metavariables of type ``Dom``:
```
PredV1 ?0 (UsePN ?1 John) (ComplV2 ?2 ?3 play (UsePN ?4 Golf))
PredV1 ?0 (UsePN ?1 Golf) (ComplV2 ?2 ?3 play (UsePN ?4 John))
```
But only the former sentence passes the type checker, which moreover
infers the domain arguments:
```
PredV1 human (UsePN human John) (ComplV2 human game play (UsePN game Golf))
```
A known problem with selectional restrictions is that they can be more
or less liberal. For instance,
```
John loves Mary
John loves golf
```
both make sense, even though ``Mary`` and ``golf``
are of different types. A natural solution in this case is to
formalize ``love`` as a **polymorphic** verb, which takes
a human as its first argument but an object of any type as its second
argument:
```
fun love : (A : Dom) -> V2 human A ;
lin love _ = ss "loves" ;
```
Problems remain, such as **subtyping** (e.g. what
is meaningful for a ``human`` is also meaningful for
a ``man`` and a ``woman``, but not the other way round)
and the **extended use** of expressions (e.g. a metaphoric use that
makes sense of "golf plays John").
===Proof objects===
Perhaps the most well-known feature of constructive type theory is
the **Curry-Howard isomorphism**, also known as the
**propositions as types principle**. Its earliest formulations
were attempts to give semantics to the logical systems of
propositional and predicate calculus. In this section, we will consider
a more elementary example, showing how the notion of proof is useful
outside mathematics, as well.
We first define the category of unary (also known as Peano-style)
natural numbers:
```
cat Nat ;
fun Zero : Nat ;
fun Succ : Nat -> Nat ;
```
The **successor function** ``Succ`` generates an infinite
sequence of natural numbers, beginning from ``Zero``.
We then define what it means for a number //x// to be less than
a number //y//. Our definition is based on two axioms:
- ``Zero`` is less than ``Succ y`` for any ``y``.
- If ``x`` is less than ``y``, then``Succ x`` is less than ``Succ y``.
The most straightforward way of expressing these axioms in type theory
is as typing judgements that introduce objects of a type ``Less x y``:
```
cat Less Nat Nat ;
fun lessZ : (y : Nat) -> Less Zero (Succ y) ;
fun lessS : (x,y : Nat) -> Less x y -> Less (Succ x) (Succ y) ;
```
Objects formed by ``lessZ`` and ``lessS`` are
called **proof objects**: they establish the truth of certain
mathematical propositions.
For instance, the fact that 2 is less that
4 has the proof object
```
lessS (Succ Zero) (Succ (Succ (Succ Zero)))
(lessS Zero (Succ (Succ Zero)) (lessZ (Succ Zero)))
```
whose type is
```
Less (Succ (Succ Zero)) (Succ (Succ (Succ (Succ Zero))))
```
which is the same thing as the proposition that 2 is less than 4.
GF grammars can be used to provide a **semantic control** of
well-formedness of expressions. We have already seen examples of this:
the grammar of well-formed addresses and the grammar with
selectional restrictions above. By introducing proof objects
we have now added a very powerful
technique of expressing semantic conditions.
A simple example of the use of proof objects is the definition of
well-formed //time spans//: a time span is expected to be from an earlier to
a later time:
```
from 3 to 8
```
is thus well-formed, whereas
```
from 8 to 3
```
is not. The following rules for spans impose this condition
by using the ``Less`` predicate:
```
cat Span ;
fun span : (m,n : Nat) -> Less m n -> Span ;
```
===Variable bindings===
Mathematical notation and programming languages have lots of
expressions that **bind** variables. For instance,
a universally quantifier proposition
```
(All x)B(x)
```
consists of the **binding** ``(All x)`` of the variable ``x``,
and the **body** ``B(x)``, where the variable ``x`` is
said to occur bound.
Variable bindings appear in informal mathematical language as well, for
instance,
```
for all x, x is equal to x
the function that for any numbers x and y returns the maximum of x+y
and x*y
```
In type theory, variable-binding expression forms can be formalized
as functions that take functions as arguments. The universal
quantifier is defined
```
fun All : (Ind -> Prop) -> Prop
```
where ``Ind`` is the type of individuals and ``Prop``,
the type of propositions. If we have, for instance, the equality predicate
```
fun Eq : Ind -> Ind -> Prop
```
we may form the tree
```
All (\x -> Eq x x)
```
which corresponds to the ordinary notation
```
(All x)(x = x).
```
An abstract syntax where trees have functions as arguments, as in
the two examples above, has turned out to be precisely the right
thing for the semantics and computer implementation of
variable-binding expressions. The advantage lies in the fact that
only one variable-binding expression form is needed, the lambda abstract
``\x -> b``, and all other bindings can be reduced to it.
This makes it easier to implement mathematical theories and reason
about them, since variable binding is tricky to implement and
to reason about. The idea of using functions as arguments of
syntactic constructors is known as **higher-order abstract syntax**.
The question now arises: how to define linearization rules
for variable-binding expressions?
Let us first consider universal quantification,
```
fun All : (Ind -> Prop) -> Prop.
```
We write
```
lin All B = {s = "(" ++ "All" ++ B.$0 ++ ")" ++ B.s}
```
to obtain the form shown above.
This linearization rule brings in a new GF concept - the ``v``
field of ``B`` containing a bound variable symbol.
The general rule is that, if an argument type of a function is
itself a function type ``A -> C``, the linearization type of
this argument is the linearization type of ``C``
together with a new field ``$0 : Str``. In the linearization rule
for ``All``, the argument ``B`` thus has the linearization
type
```
{$0 : Str ; s : Str},
```
since the linearization type of ``Prop`` is
```
{s : Str}
```
(we remind that the order of fields in a record does not matter).
In other words, the linearization of a function
consists of a linearization of the body together with a
field for a linearization of the bound variable.
Those familiar with type theory or lambda calculus
should notice that GF requires trees to be in
**eta-expanded** form in order to be linearizable:
any function of type
```
A -> C
```
always has a syntax tree of the form
```
\x -> c
```
where ``c : C`` under the assumption ``x : A``.
It is in this form that an expression can be analysed
as having a bound variable and a body.
Given the linearization rule
```
lin Eq a b = {s = "(" ++ a.s ++ "=" ++ b.s ++ ")"}
```
the linearization of
```
\x -> Eq x x
```
is the record
```
{$0 = "x", s = ["( x = x )"]}
```
Thus we can compute the linearization of the formula,
```
All (\x -> Eq x x) --> {s = "[( All x ) ( x = x )]"}.
```
How did we get the //linearization// of the variable ``x``
into the string ``"x"``? GF grammars have no rules for
this: it is just hard-wired in GF that variable symbols are
linearized into the same strings that represent them in
the print-out of the abstract syntax.
To be able to
//parse// variable symbols, however, GF needs to know what
to look for (instead of e.g. trying to parse //any//
string as a variable). What strings are parsed as variable symbols
is defined in the lexical analysis part of GF parsing (see below).
When //editing// with grammars that have
bound variables, the names of bound variables are
selected automatically, but can be changed at any time by
using an Alpha Conversion command.
If several variables are bound in the same argument, the
labels are ``$0, $1, $2``, etc.
===Semantic definitions=== ===Semantic definitions===
===List categories=== We have seen that,
just like functional programming languages, GF has declarations
of functions, telling what the type of a function is.
But we have not yet shown how to **compute**
these functions: all we can do is provide them with arguments
and linearize the resulting terms.
Since our main interest is the well-formedness of expressions,
this has not yet bothered
us very much. As we will see, however, computation does play a role
even in the well-formedness of expressions when dependent types are
present.
GF has a form of judgement for **semantic definitions**,
recognized by the key word ``def``. At its simplest, it is just
the definition of one constant, e.g.
```
def one = succ zero ;
```
We can also define a function with arguments,
```
def Neg A = Impl A Abs ;
```
which is still a special case of the most general notion of
definition, that of a group of **pattern equations**:
```
def sum x zero = x ;
def sum x (succ y) = succ (sum x y) ;
```
To compute a term is, as in functional programming languages,
simply to follow a chain of reductions until no definition
can be applied. For instance, we compute
```
sum one one -->
sum (succ zero) (succ zero) -->
succ (sum (succ zero) zero) -->
succ (succ zero)
```
The ``def`` definitions of a grammar induce a notion of
**definitional equality** among trees: two trees are
definitionally equal if they compute into the same tree.
Thus, trivially, all trees in a chain of computation
(such as the one above)
are definitionally equal to each other. So are the trees
```
sum zero (succ one)
succ one
sum (sum zero zero) (sum (succ zero) one)
```
and infinitely many other trees.
A fact that has to be emphasized about ``def`` definitions is that
they are //not// performed as a first step of linearization.
We say that **linearization is intensional**, which means that
the definitional equality of two trees does not imply that
they have the same linearizations. For instance, the seven terms
above all have different linearizations in arithmetic notation:
```
1 + 1
s(0) + s(0)
s(s(0) + 0)
s(s(0))
0 + s(0)
s(1)
0 + 0 + s(0) + 1
```
This notion of intensionality is
no more exotic than the intensionality of any **pretty-printing**
function of a programming language (function that shows
the expressions of the language as strings). It is vital for
pretty-printing to be intensional in this sense - if we want,
for instance, to trace a chain of computation by pretty-printing each
intermediate step, what we want to see is a sequence of different
expression, which are definitionally equal.
What is more exotic is that GF has two ways of referring to the
abstract syntax objects. In the concrete syntax, the reference is intentional.
In the abstract syntax itself, the reference is always extensional, since
**type checking is extensional**. The reason is that,
in the type theory with dependent types, types may depend on terms.
Two types depending on terms that are definitionally equal are
equal types. For instance,
```
Proof (Od one)
Proof (Od (succ zero))
```
are equal types. Hence, any tree that type checks as a proof that
1 is odd also type checks as a proof that the successor of 0 is odd.
(Recall, in this connection, that the
arguments a category depends on never play any role
in the linearization of trees of that category,
nor in the definition of the linearization type.)
In addition to computation, definitions impose a
**paraphrase** relation on expressions:
two strings are paraphrases if they
are linearizations of trees that are
definitionally equal.
Paraphrases are sometimes interesting for
translation: the **direct translation**
of a string, which is the linearization of the same tree
in the targer language, may be inadequate because it is e.g.
unidiomatic or ambiguous. In such a case,
the translation algorithm may be made to consider
translation by a paraphrase.
To stress express the distinction between
**constructors** (=**canonical** functions)
and other functions, GF has a judgement form
``data`` to tell that certain functions are canonical, e.g.
```
data Nat = succ | zero ;
```
Unlike in Haskell, but similarly to ALF (where constructor functions
are marked with a flag ``C``),
new constructors can be added to
a type with new ``data`` judgements. The type signatures of constructors
are given separately, in ordinary ``fun`` judgements.
%--! %--!