[[!toc levels=2]] The Reader Monad ================ The goal for this part is to introduce the Reader Monad, and present two linguistics applications: binding and intensionality. Along the way, we'll continue to think through issues related to order, and a related notion of flow of information. At this point, we've seen monads in general, and three examples of monads: the identity monad (invisible boxes), the Maybe monad (option types), and the List monad. We've also seen an application of the Maybe monad to safe division. The starting point was to allow the division function to return an int option instead of an int. If we divide 6 by 2, we get the answer Just 3. But if we divide 6 by 0, we get the answer Nothing. The next step was to adjust the other arithmetic functions to teach them what to do if they received Nothing instead of a boxed integer. This meant changing the type of their input from ints to int options. But we didn't need to do this piecemeal; rather, we could "lift" the ordinary arithmetic operations into the monad using the various tools provided by the monad. We'll go over this lifting operation in detail in the next section. ## Tracing the effect of safe-div on a larger computation So let's see how this works in terms of a specific computation.
```\tree ((((+) (1)) (((*) (((/) (6)) (2))) (4))))

___________
|         |
_|__    ___|___
|  |    |     |
+  1  __|___  4
|    |
*  __|___
|    |
_|__  2
|  |
/  6
```
This computation should reduce to 13. But given a specific reduction strategy, we can watch the order in which the computation proceeds. Following on the lambda evaluator developed during the previous homework, let's adopt the following reduction strategy: In order to reduce (head arg), do the following in order: 1. Reduce head to h' 2. Reduce arg to a'. 3. If (h' a') is a redex, reduce it. There are many details left unspecified here, but this will be enough for today. The order in which the computation unfolds will be 1. Reduce head (+ 1) to itself 2. Reduce arg ((* ((/ 6) 2)) 3) 1. Reduce head (* ((/ 6) 2)) 1. Reduce head * 2. Reduce arg ((/ 6) 2) 1. Reduce head (/ 6) to itself 2. Reduce arg 2 to itself 3. Reduce ((/ 6) 2) to 3 3. Reduce (* 3) to itself 2. Reduce arg 4 to itself 3. Reduce ((* 3) 4) to 12 3. Reduce ((+ 1) 12) to 13 This reduction pattern follows the structure of the original expression exactly, at each node moving first to the left branch, processing the left branch, then moving to the right branch, and finally processing the results of the two subcomputation. (This is called depth-first postorder traversal of the tree.) It will be helpful to see how the types change as we make adjustments. type num = int type contents = Num of num | Op of (num -> num -> num) type tree = Leaf of contents | Branch of tree * tree Never mind that these types will allow us to construct silly arithmetric trees such as `+ *` or `2 3`. Note that during the reduction sequence, the result of reduction was in every case a well-formed subtree. So the process of reduction could be animated by replacing subtrees with the result of reduction on that subtree, till the entire tree is replaced by a single integer (namely, 13). Now we replace the number 2 with 0:
```\tree ((((+) (1)) (((*) (((/) (6)) (0))) (4))))

___________
|         |
_|__    ___|___
|  |    |     |
+  1  __|___  4
|    |
*  __|___
|    |
_|__  0
|  |
/  6
```
When we reduce, we get quite a ways into the computation before things go south: 1. Reduce head (+ 1) to itself 2. Reduce arg ((* ((/ 6) 0)) 3) 1. Reduce head (* ((/ 6) 0)) 1. Reduce head * 2. Reduce arg ((/ 6) 0) 1. Reduce head (/ 6) to itself 2. Reduce arg 0 to itself 3. Reduce ((/ 6) 0) to ACKKKK This is where we replace `/` with `safe-div`. This means changing the type of the arithmetic operators from `int -> int -> int` to `int -> int -> int option`; and since we now have to anticipate the possibility that any argument might involve division by zero inside of it, here is the net result for our types: type num = int option type contents = Num of num | Op of (num -> num -> num) type tree = Leaf of contents | Branch of tree * tree The only difference is that instead of defining our numbers to be simple integers, they are now int options; and so Op is an operator over int options. At this point, we bring in the monadic machinery. In particular, here is the ⇧ and the map2 function from the notes on safe division: ⇧ (a: 'a) = Just a;; map2 (g : 'a -> 'b -> 'c) (u : 'a option) (v : 'b option) = match u with | None -> None | Some x -> (match v with | None -> None | Some y -> Some (g x y));; Then we lift the entire computation into the monad by applying ⇧ to the integers, and by applying `map1` to the operators:
```\tree ((((map2 +) (⇧1)) (((map2 *) (((map2 /) (⇧6)) (⇧0))) (⇧4))))

___________________
|                 |
___|____         ____|_____
|      |         |        |
map2 +  ⇧1    _____|_____  ⇧4
|         |
map2 *  ____|____
|       |
___|____  ⇧0
|      |
map2 /  ⇧6
```
With these adjustments, the faulty computation now completes smoothly: 1. Reduce head ((map2 +) ⇧1) 2. Reduce arg (((map2 *) (((map2 /) ⇧6) ⇧2)) ⇧3) 1. Reduce head ((map2 *) (((map2 /) ⇧6) ⇧2)) 1. Reduce head * 2. Reduce arg (((map2 /) ⇧6) ⇧0) 1. Reduce head ((map2 /) ⇧6) 2. Reduce arg ⇧0 3. Reduce (((map2 /) ⇧6) ⇧0) to Nothing 3. Reduce ((map2 *) Nothing) to Nothing 2. Reduce arg ⇧4 3. Reduce (((map2 *) Nothing) ⇧4) to Nothing 3. Reduce (((map2 +) ⇧1) Nothing) to Nothing As soon as we try to divide by 0, safe-div returns Nothing. Thanks to the details of map2, the fact that Nothing has been returned by one of the arguments of a map2-ed operator guarantees that the map2-ed operator will pass on the Nothing as its result. So the result of each enclosing computation will be Nothing, up to the root of the tree. It is unfortunate that we need to continue the computation after encountering our first Nothing. We know immediately at the result of the entire computation will be Nothing, yet we continue to compute subresults and combinations. It would be more efficient to simply jump to the top as soon as Nothing is encoutered. Let's call that strategy Abort. We'll arrive at an Abort operator later in the semester. So at this point, we can see how the Maybe/option monad provides plumbing that allows subcomputations to send information from one part of the computation to another. In this case, the safe-div function can send the information that division by zero has been attempted throughout the rest of the computation. If you think of the plumbing as threaded through the tree in depth-first, postorder traversal, then safe-div drops Nothing into the plumbing half way through the computation, and that Nothing travels through the rest of the plumbing till it comes out of the result faucet at the top of the tree. ## Information flowing in the other direction: top to bottom In the save-div example, a subcomputation created a message that propagated upwards to the larger computation:
```                 message: Division by zero occurred!
^
___________          |
|         |          |
_|__    ___|___       |
|  |    |     |       |
+  1  __|___  4       |
|    |          |
*  __|___  -----|
|    |
_|__  0
|  |
/  6
```
We might want to reverse the direction of information flow, making information available at the top of the computation available to the subcomputations:
```                    [λx]
___________          |
|         |          |
_|__    ___|___       |
|  |    |     |       |
+  1  __|___  4       |
|    |          |
*  __|___       |
|    |       |
_|__  x  <----|
|  |
/  6
```
```\tree ((((map2 +) (⇧1)) (((map2 *) (((map2 /) (⇧6)) (x))) (⇧4))))

__________________
|                |
___|____        ____|_____
|      |        |        |
map2 +  ⇧1    ____|_____  ⇧4
|        |
map2 *  ___|____
|      |
___|____  x
|      |
map2 /  ⇧6
```
It remains only to decide how the variable `x` will access the value input at the top of the tree. Since the input value is supposed to be the value put in place of the variable `x`. Like every leaf in the tree in argument position, the code we want in order to represent the variable will have the type of a boxed int, namely, `int -> int`. So we have the following: x (i:int):int = i That is, variables in this system denote the indentity function! The result of evaluating this tree will be a boxed integer: a function from any integer `x` to `(+ 1 (* (/ 6 x)) 4)`. Take a look at the definition of the reader monad again. The midentity takes some object `a` and returns `\x.a`. In other words, `⇧a = Ka`, so `⇧ = K`. Likewise, `map2` for this monad is the `S` combinator. We've seen this before as a strategy for translating a lambda abstract into a set of combinators. Here is a part of the general scheme for translating a lambda abstract into Combinatory Logic. The translation function `[.]` translates a lambda term into a term in Combinatory Logic: [(MN)] = ([M] [N]) [\a.a] = I [\a.M] = K[M] (assuming a not free in M) [\a.(MN)] = S[\a.M][\a.N] The reason we can make do with this subset of the full function is that we're making the simplifying assumption that there is at most a single lambda involved. So here you see the I (the translation of the bound variable), the K and the S. ## Jacobson's Variable Free Semantics as a Reader Monad We've designed the discussion so far to make the following claim as easy to show as possible: Jacobson's Variable Free Semantics (e.g., Jacobson 1999, [Towards a Variable-Free Semantics](http://www.springerlink.com/content/j706674r4w217jj5/)) is a reader monad. More specifically, it will turn out that Jacobson's geach combinator *g* is exactly our `lift` operator, and her binding combinator *z* is exactly our `bind` (though with the arguments reversed)! Jacobson's system contains two main combinators, *g* and *z*. She calls *g* the Geach rule, and *z* performs binding. Here is a typical computation. This implementation is based closely on email from Simon Charlow, with beta reduction as performed by the on-line evaluator:
```; Analysis of "Everyone_i thinks he_i left"
let g = \f g x. f (g x) in
let z = \f g x. f (g x) x in
let he = \x. x in
let everyone = \P. FORALL x (P x) in

everyone (z thinks (g left he))

~~>  FORALL x (thinks (left x) x)
```
Several things to notice: First, pronouns once again denote identity functions. As Jeremy Kuhn has pointed out, this is related to the fact that in the mapping from the lambda calculus into combinatory logic that we discussed earlier in the course, bound variables translated to I, the identity combinator (see additional comments below). We'll return to the idea of pronouns as identity functions in later discussions. Second, *g* plays the role of transmitting a binding dependency for an embedded constituent to a containing constituent. Third, one of the peculiar aspects of Jacobson's system is that binding is accomplished not by applying *z* to the element that will (in some pre-theoretic sense) bind the pronoun, here, *everyone*, but rather by applying *z* instead to the predicate that will take *everyone* as an argument, here, *thinks*. The basic recipe in Jacobson's system, then, is that you transmit the dependence of a pronoun upwards through the tree using *g* until just before you are about to combine with the binder, when you finish off with *z*. (There are examples with longer chains of *g*'s below.) Jacobson's *g* combinator is exactly our `lift` operator: it takes a functor and lifts it into the monad. Furthermore, Jacobson's *z* combinator, which is what she uses to create binding links, is essentially identical to our reader-monad `bind`!
```everyone (z thinks (g left he))

~~> forall w (thinks (left w) w)

everyone (z thinks (g (t bill) (g said (g left he))))

~~> forall w (thinks (said (left w) bill) w)
```
(The `t` combinator is given by `t x = \xy.yx`; it handles situations in which English word order places the argument (in this case, a grammatical subject) before the predicate.) So *g* is exactly `lift` (a combination of `bind` and `unit`), and *z* is exactly `bind` with the arguments reversed. It appears that Jacobson's variable-free semantics is essentially a Reader monad. ## The Reader monad for intensionality Now we'll look at using monads to do intensional function application. This is just another application of the Reader monad, not a new monad. In Shan (2001) [Monads for natural language semantics](http://arxiv.org/abs/cs/0205026v1), Ken shows that making expressions sensitive to the world of evaluation is conceptually the same thing as making use of the Reader monad. This technique was beautifully re-invented by Ben-Avi and Winter (2007) in their paper [A modular approach to intensionality](http://parles.upf.es/glif/pub/sub11/individual/bena_wint.pdf), though without explicitly using monads. All of the code in the discussion below can be found here: [[code/intensionality-monad.ml]]. To run it, download the file, start OCaml, and say # #use "intensionality-monad.ml";; Note the extra `#` attached to the directive `use`. First, the familiar linguistic problem: Bill left. Cam left. Ann believes [Bill left]. Ann believes [Cam left]. We want an analysis on which the first three sentences can be true at the same time that the last sentence is false. If sentences denoted simple truth values or booleans, we have a problem: if the sentences *Bill left* and *Cam left* are both true, they denote the same object, and Ann's beliefs can't distinguish between them. The traditional solution to the problem sketched above is to allow sentences to denote a function from worlds to truth values, what Montague called an intension. So if `s` is the type of possible worlds, we have the following situation:
```Extensional types              Intensional types       Examples
-------------------------------------------------------------------

S         t                    s->t                    John left
DP        e                    s->e                    John
VP        e->t                 (s->e)->s->t            left
Vt        e->e->t              (s->e)->(s->e)->s->t    saw
Vs        t->e->t              (s->t)->(s->e)->s->t    thought
```
This system is modeled on the way Montague arranged his grammar. There are significant simplifications compared to Montague: for instance, determiner phrases are thought of here as corresponding to individuals rather than to generalized quantifiers. The main difference between the intensional types and the extensional types is that in the intensional types, the arguments are functions from worlds to extensions: intransitive verb phrases like "left" now take so-called "individual concepts" as arguments (type s->e) rather than plain individuals (type e), and attitude verbs like "think" now take propositions (type s->t) rather than truth values (type t). In addition, the result of each predicate is an intension. This expresses the fact that the set of people who left in one world may be different than the set of people who left in a different world. Normally, the dependence of the extension of a predicate to the world of evaluation is hidden inside of an evaluation coordinate, or built into the the lexical meaning function, but we've made it explicit here in the way that the intensionality monad makes most natural. The intensional types are more complicated than the extensional types. Wouldn't it be nice to make the complicated types available for those expressions like attitude verbs that need to worry about intensions, and keep the rest of the grammar as extensional as possible? This desire is parallel to our earlier desire to limit the concern about division by zero to the division function, and let the other functions, like addition or multiplication, ignore division-by-zero problems as much as possible. So here's what we do: In OCaml, we'll use integers to model possible worlds. Characters (characters in the computational sense, i.e., letters like `'a'` and `'b'`, not Kaplanian characters) will model individuals, and OCaml booleans will serve for truth values: type s = int;; type e = char;; type t = bool;; let ann = 'a';; let bill = 'b';; let cam = 'c';; let left1 (x:e) = true;; let saw1 (x:e) (y:e) = y < x;; left1 ann;; (* true *) saw1 bill ann;; (* true *) saw1 ann bill;; (* false *) So here's our extensional system: everyone left, including Ann; and Ann saw Bill (`saw1 bill ann`), but Bill didn't see Ann. (Note that the word order we're using is VOS, verb-object-subject.) Now we add intensions. Because different people leave in different worlds, the meaning of *leave* must depend on the world in which it is being evaluated: let left (x:e) (w:s) = match (x, w) with ('c', 2) -> false | _ -> true;; left ann 1;; (* true: Ann left in world 1 *) left cam 2;; (* false: Cam didn't leave in world 2 *) This new definition says that everyone always left, except that in world 2, Cam didn't leave. Note that although this general *left* is sensitive to world of evaluation, it does not have the fully intensionalized type given in the chart above, which was `(s->e)->s->t`. This is because *left* does not exploit the additional resolving power provided by making the subject an individual concept. In semantics jargon, we say that *leave* is extensional with respect to its first argument. Therefore we will adopt the general strategy of defining predicates in a way that they take arguments of the lowest type that will allow us to make all the distinctions the predicate requires. When it comes time to combine this predicate with monadic arguments, we'll have to make use of various lifting predicates. Likewise, although *see* depends on the world of evaluation, it is extensional in both of its syntactic arguments: let saw x y w = (w < 2) && (y < x);; saw bill ann 1;; (* true: Ann saw Bill in world 1 *) saw bill ann 2;; (* false: no one saw anyone in world 2 *) This (again, partially) intensionalized version of *see* coincides with the `saw1` function we defined above for world 1; in world 2, no one saw anyone. Just to keep things straight, let's review the facts:
```     World 1: Everyone left.
Ann saw Bill, Ann saw Cam, Bill saw Cam, no one else saw anyone.
World 2: Ann left, Bill left, Cam didn't leave.
No one saw anyone.
```
```type 'a intension = s -> 'a;;
```lift2' saw (unit bill) (unit ann) 1;;  (* true *)