Real science data is not natural. It is artificial. It is
rarely encountered in the wild and (as Nancy Cartwright has emphasized (see
here for discussion)) it standardly takes a lot of careful work to create the
conditions in which the facts are observable. The idea that science proceeds by
looking carefully at the natural world is deeply misleading, unless, of course,
the world you inhabit happens to be CERN. I mention this because one of the
hallmarks of a progressive research program is that it supports the manufacture
of such novel artificial data and their bundling into large scale “effects,” artifacts
which then become the targets of theoretical speculation.[1]
Indeed, one measure of how far a science has gotten is the degree to which the
data it concerns itself with is factitious and the number of well-established
effects it has managed to manufacture. Actually, I am tempted to go further: as
a general rule only very immature scientific endeavors are based on naturally
available/occurring facts.[2]
Why do I mention this. Well, first, by this measure,
Generative Grammar (GG) has been a raging success. I have repeatedly pointed to
the large number of impressive effects that GG has collected over the last 60
years and the interesting theories that GGers have developed trying to explain
them (e.g. here).
Island and ECP effects, binding effects and WCO effects do not arise naturally
in language use. They need to be constructed, and in this they are like most facts
of scientific interest.
Second, one nice way to get a sense of what is happening in
a nearby domain is to zero in on the effects its practitioners are addressing.
Actually, more pointedly, one quick and dirty way of seeing whether some area
is worth spending time on is to canvass the variety and number of different
effects it has manufactured. In what
follows I would like to discuss one of these that has recently come to my
attention that has some interests for a GGer like me.
A recent paper (here)
by Jiwon Yun, Zhong Chen, Tim Hunter, John Whitman and John Hale (YCHWH)
discusses an interesting processing fact concerning relative clauses (RC) that
seems to hold robustly cross linguistically. The effect is called the “Subject
Advantage” (SA). What’s interesting about this effect is that it holds in
languages where the head both precedes and follows the relative clause (i.e.
for languages like English and those like Japanese). Why is this
interesting?
Well, first, this argues against the idea that the SA simply
reflects increasing memory load as a function of linear distance between gap
and filler (i.e. head). This cannot be the relevant variable for though it
could account for SA effects in languages like English where the head precedes
the RC (thus making the subject gap closer to the head than the object gap is)
in Japanese style RCs where heads follow the clause the object gap is linearly
closer to the head than the subject gap is, hence predicting an object
advantage, contrary to experimental fact.
Second, and
here let me quote John Hale (p.c.):
SA effects defy explanation in terms of
"surprisal". The surprisal idea is that low probability words are
harder, in context. But in relative clauses surprisal values from simple
phrase structure grammars either predict effort on the wrong word (Hale 2001) or get it completely backwards --- an object
advantage, rather than a subject advantage (Levy 2008, page 1164).
Thus, SA effects are interesting in that they appear to be
stable over languages as diverse as English on the one hand and Japanese on the
other and seem to refractory to many of the usual processing explanations.
Furthermore, SA effects suggest that grammatical structure
is important, or to put this in more provocative terms, that SA effects are
structure dependent in some way. Note that this does not imply that SA effects are grammatical effects, only that G
structure is implicated in their explanation.
In this, SA effects are a little like Island Effects as understood (here).[3]
Purely functional stories that ignore G structure (e.g. like linearly dependent
memory load or surprisal based on word-by-word processing difficulty) seem to
be insufficient to explain these effects (see YCHWH 117-118).[4]
So how to explain the SA? YCHWH proposes an interesting
idea: that what makes object relatives harder than subject relatives is have
different amounts of “sentence medial ambiguity” (the former more than the
latter) and that resolving this ambiguity takes work that is reflected in
processing difficulty. Or put more flatfootedly, finding an object gap requires
getting rid of more grammatical
ambiguity than finding a subject gap and getting rid of this ambiguity requires
work, which is reflected in processing difficulty. That’s the basic idea. He
work is in the details that YCHWH provides. And there are a lot of them. Here are some.
YCHWH defines a notion of “Entropy Reduction” based on the
weighted possible continuations available at a given point in a parse. One
feature of this is that the model provides a way of specifying how much work
parsing is engaged in at a particular
point. This contrasts with, for example, a structural measure of memory
load. As note 4 observes, such a measure could explain a subject advantage but
as John Hale (p.c.) has pointed out to me concerning this kind of story:
This general account is thus adequate but not very
precise. It leaves open, for instance, the question of where exactly greater
difficulty should start to accrue during incremental processing.
That said, whether to go for the YCHWH account or the less
precise structural memory load account is ultimately an empirical matter.[5]
One thing that YCHWH suggests is that it should be possible to obviate the SA
effect given the right kind of corpus data. Here’s what I mean.
YCHWH defines entropy reduction by (i) specifying a G for a
language that defines the possible G continuations in that language and (ii)
assigning probabilistic weights to these continuations. Thusm YCHWH shows how
to combine Gs and probabilities of use of these. Parsing, not surprisingly,
relies on the details of a particular
G and the details of the corpus of usages of those G possibilities. Thus, what
options a particular G allows affects how much entropy reduction a given word
licenses, as does the details of the corpus that are probabilize the G. This thus means that it is possible that SA
might disappear given the right corpus details. Or it allows us to ask what if
any corpus details could wipe out SA effects. This, as Tim Hunter noted (p.c.)
raises two possibilities. In his words:
An interesting (I think) question that arises is:
what, if any, different patterns of corpus data would wipe out the subject
advantage? If the answer were 'none', then that would mean that the grammar
itself (i.e. the choice of rules) was the driving force. This is almost
certainly not the case. But, at the other extreme, if the answer were 'any
corpus data where SRCs are less frequent than ORCs', then one would be forgiven
for wondering whether the grammar was doing anything at all, i.e. wondering
whether this whole grammar-plus-entropy-reduction song and dance were just a
very roundabout way of saying "SRCs are easier because you hear them more
often".
One of the nice features of the YCHWH discussion is that it
makes it possible to analytically
approach this problem. It would be nice to know what the answer is both
analytically as well as empirically.
Another one of he nice features of YCHWH is that it
demonstrates how to probabilize MGs of the Stabler variety so that one can view
parsing as a general kind of information
processing problem. In such a context difficulties in language parsing are
the natural result of general information processing demands. Thus, this
conception of parsing locates it in a more general framework of information
processing, parsing being one specific application where the problem is to
determine the possible G compatible continuations of a sentence. Note that this
provides a general model of how G knowledge can get used to perform some task.
Interestingly, on this view, parsing does not require a
parser. Why? Because parsing just is information processing when the relevant
information is fixed. It’s not like we do language parsing differently than we
do, say, visual scene interpretation once
we fix the relevant structures being manipulated. In other words, parsing
on the YCHWH view is just information
processing in the domain of language (i.e. there is nothing special about language processing except the fact that
it is Gish structures that are being manipulated). Or, to say this another way,
though we have lots of parsing, there is no parser that does it.
YCHWH is a nice
example of a happy marriage of grammar and probabilities to explain an
interesting parsing effect, the SA. The latter is a discovery about the ease of
parsing RCs that suggests that G structure matters and that language
independent functional considerations just won’t cut it. It also shows how easy
it is to combine MGs with corpora to deliver probabilistic Gs that are
plausibly useful in language use. All in all, fun stuff, and very instructive.
[2]
This is one reason why I find admonitions to focus on natural speech as a
source of linguistic data to be bad advice in general. There may be exceptions,
but as a general rule such data should be treated very gingerly.
[3]
See, for example, the discussion in the paper by Sprouse, Wagers and Phillips.
[4]
A measure of distance based on structure could explain the SA. For example, there
are more nodes separating the object trace and the head than separating the
subject trace and the head. If memory load were a function of depth of
separation, that could account for the SA, at least at the whole sentence level.
However, until someone
defines an incremental version of the Whole-Sentence structural memory load
theory, it seems that only Entropy Reduction can account for the word-by-word
SA effect across both English-type and Japanese-type languages.
[5]
The following is based on some correspondence with Tim Hunter. Thus he is
entirely responsible for whatever falsehoods creep into the discussion here.
