Should AI research be concerned with explicit representations of the meanings of utterances? By “explicit representations of meaning” I mean structured variables with a pre-defined interpretation—the kind of thing that semanticists are concerned with. For a long time, such meaning representations were central to successful efforts aimed at linking language to other tasks involving reasoning, perception, and action (a line of work that runs from SHRDLU to modern semantic parsers). Structured meaning representations were also central to unsuccessful work on machine translation, syntax, etc. This work uses lots of different representation formalisms—proper neo-Davidsonian logical forms [AZ13], combinator logics [LJK11], other non-logical structures [TK+11]—but if you squint they’re all basically predicate-argument structures implementing a model-theoretic semantics, with perhaps a few free parameters in the bodies of the predicates.
These kinds of approaches seem to be disappearing. Now that everything is
end-to-end all the time, it’s rare to see models with explicit latent variables
taking values in hand-designed logical languages. Utterances come into our
models, behaviors come out, and we don’t worry too much about the structure of
the computation that gets performed in the middle.
There are still certain kinds of generalization and inductive bias we used to get for free with the old models that we haven’t totally figured out how to recreate. The success of hybrid approaches like structured regularizers [OS+17] and our NMN work [AR+16] suggest we’ll get there eventually. This is, with some qualifications, a good thing: in more formal approaches, tight coupling between the machine learning and the representation means that there’s always a risk that some new semantic phenomenon shows up in the data and suddenly your model is useless. Sufficiently generic machinery for learning (non-logical) representations makes this a little less scary.
But the attitude of the end-to-end world seems to be that since we’re no longer
doing logical inference, there’s no need to think about meaning at all. Suddenly
everyone loves to cite Wittgenstein to argue that we should be
evaluating “language understanding” in terms of success on downstream tasks
rather than predicting the right logical forms
[LPB16]—which is great!—but underlying
this seems to be a philosophy that “meaning is use, so if we can predict use with
high accuracy we’ve understood everything we need to about meaning”.
I’ve never understood this to be the claim in Philosophical
Investigations—even if use (rather than reference) is the primary thing we
should be trying to explain, PI is very interested in the kinds of
representations representations of the processes (?) in virtue of which
language use is possible.
Particularly given that we have not actually solved “use”, I think machine
learning has both lots to learn and lots to say about the meaning side of the
equation as well.
In this post I want to motivate the use of explicit representations of belief states of the form as representations of meaning suitable for “unstructured” machine learning models. These kinds of representations arise naturally in the sorts of decision-making tasks the community is excited about about these days, but they also look a lot like classical representational theories in linguistics. The synthesis suggests ways of both training and interpreting language processing models.
Belief states and intensions
Consider the problem of trying to act in a partially observed world where people talk to you in order to help reduce your uncertainty. How should you choose the best possible action to take? Given a single utterance , and possible true states of the world , the min Bayes risk action is
for some risk function . Any listener who hopes to succeed in the world needs to do a good job of at least approximately solving this optimization problem, and in practice the listener will probably need to represent the distribution , at least implicitly. In POMDP-land we call a belief state; for a given , it’s a function that maps from possible worlds to scalar plausibility judgments—how likely is it that is the true world given that we observed someone saying about it?
Compare this to the notion of an intension in Montague semantics: “a function from possible worlds and moments of time to truth values” [J11]. Most (model-theoretic) semantics programs represent intensions using logical expressions (rather than e.g. tabularly). But a logical form is just one way of expressing a function of the right type; at the end of the day, an “explicit representation of meaning” to the Montagovian tradition is precisely an intension—that is, something that looks like a discretized version of our .
A belief state is an intension with probabilities. Intensional representations of meaning are useful not just because they help us solve linguistic problems, but also because they approximate a quantity that we know helps language users do useful things with the information they’ve acquired from language. From the other side, what the POMDP tells us we have to compute upon hearing an utterance is approximately the thing linguists have been telling us to compute all along. Or almost the thing linguists have been telling us about—what would be even better than a black box for answering queries would be something with a little structure, maybe some kind of factorized representation that let us find the MBR action efficiently by making it possible to inspect the set of properties that all plausible worlds have in common. Perhaps a product of assertions about individuals, their properties, and their relations…. If logical semantics didn’t exist we would have had to invent it.
qua “meaning” should be precisely understood as a listener meaning: an accurate belief state already accounts for Gricean speaker-meaning-type effects (e.g. implicatures) and also further inferences the speaker may not have wanted the listener to draw (e.g. the possibility that is a lie). Our story here doesn’t care where comes from, so it might be computed via something like RSA [FG12] with a distinct notion of sentence meaning embedded inside.
One last adjustment: real-world listeners don’t start with a tabula rasa: every utterance is interpreted in the context of an existing belief state , and we really want to think of the meaning of a sentence as an update function; i.e. rather than just . For sentences of the “Pat loves Lou” variety I think this update is basically always conjunctive; i.e. . But the general version is necessary for dealing with indexicals and Quine’s problems with the denotation of bachelor.
All very nice, but we led by noting that explicit denotational meaning representations (logical, probabilistic, or otherwise) don’t actually show up in the kinds of models that work well in practice. So why does any of this matter?
For language understanding systems to work well, they must be choosing something close to the min Bayes risk action. Hand-wavingly: a suffix of a deep network is a function from input representations to output actions via a fixed circuit; if this suffix can pick a good action for every input representation it’s implementing something like an MBR decoding algorithm (though perhaps approximate and specialized to the empirical distribution over representations); whatever representation of language-in-context is presented to this part of the network must then be sufficient to solve the optimization problem, so be something like a representation of .
This is not a great argument: there may in fact be no clear distinction between the “sentence representation” and “optimization” parts of the model. But in practice we do see meaning-like sentence representations emerge (especially in models where the sentence representation is computed independent of whatever initial information the listener has about the state of the world [DP+18]). When using specialized optimization modules within larger networks [TW+17] [LFK18] we can be sure of the distinction.
In any case, the knowledge that some intermediate representation in our model is (or should be) decodable into a distribution over world states gives us two tools:
Interpretability: test whether representations are capturing the right semantics (or identify what weird irregularities they’re latching onto) by estimating , where is the model’s learned representation of the utterance . Determine whether this corresponds to the real (i.e. human listener’s) denotation of . We got a bunch of mileage out of this technique in our neuralese papers [ADK17] [AK17] and other students in the group have been using it recently to analyze pretraining schemes for instruction-following models. But in some ways it’s even more natural to apply it to the learning of representations of natural language itself rather than a learned space of messages / abstract actions.
Auxiliary objectives: the normal objective for an instruction following / QA problem is . But if overfitting is an issue, it’s easy enough to tack on an extra term of the form if it’s available. For some problems (e.g. GeoQuery-style semantic parsing) there isn’t a meaningful distinction between “speaker observation” and “action”; for others it looks like a totally different learning problem. For referring expression games the denotational auxiliary problem is “generate / retrieve pairs of images for which this would be a discriminative caption”; for instruction following models it’s “generate the goal state (but not necessarily the actions that get me there)”.
Thinking about POMDP-style solutions to language games results in an account of meaning that looks suspiciously like model-theoretic semantics. This analogy provides tools for interpreting learned models and suggests auxiliary objectives for improving their accuracy.