Inference Tasks and Computational Semantics

Key Concepts Inference tasks Syntactic versus semantic approach to logic Soundness & completeness Decidability and undecidability Technologies: Theorem proving versus model building

INFERENCE TASKS QUERYING CONSISTENCY CHECKING INFORMATIVITY CHECKING

QUERYING Definition: –Given: Model M and formula P –Does M satisfy P? P is not necessarily a sentence, so have to handle assignments to free variables. Computability: yes if models are finite

Consistency Checking Definition: Given a formula P, is P consistent? Idea: consistent iff satisfiable in a model M, so task becomes discovering whether a model exists. This is a search problem. Computationally undecidable for arbitrary P.

Informativity Checking Definition: given P, is P informative or uninformative? Idea (which runs counter to logician's view) –informative = invalid –uninformative = valid (true in all possible models) Informativity: is genuinely new information being conveyed? Useful concept from PoV of communication Computability: validity worse than consistency checking since all models need to be checked for satisfiability.

Relations between Concepts P is consistent (satisfiable) iff –P is informative (invalid) P is inconsistent (unsatisfiable) iff –P uninformative (valid). P is informative (invalid) if –P is consistent (satisfiable). P is uninformative (valid) if –P is inconsistent (unsatisfiable).

Consistency within Discourse Mia smokes. Mia does not smoke. Should be possible to detect the inconsistency in such discourses To avoid detecting inconsistency in superficially similar discourses such as Mia smokes. Mia did not smoke

Consistency of Discourse w.r.t. Background Knowledge Discourse: Mia is a beautiful woman. Mia is a tree Background Knowledge: All women are human All trees are plants -Ex: human(x) and plant(x)

Consistency Checking for Resolving Scope Ambiguity Every boxer has a broken nose 1.Ax(boxer(x) -Ey(broken-nose(y) & has(x,y))) 2.Ey(broken-nose(y) & Ax(boxer(x) → has(x,y))) Second reading is inconsistent with world knowledge What world knowledge? How represented and used?

Informativity Checking Make your contribution as informative as is required (for the current purposes of the exchange). H. P. Grice. Mia smokes. Mia smokes Is not informative Informativity checking also wrt background knowledge

Informativity a `soft' signal Mia is married She has a husband Superficially uninformative wrt background knowledge. But nevertheless we can imagine contexts when such a discourse makes sense. Technically uninformative utterances can be used to “make a point”

Consistency Checking Task (CCT) in FOL Let Φ be the FOL semantic representation of the latest sentence in some ongoing discourse Suppose that the relevant lexical knowledge L, world knowledge W, natural language metaphysical assumption M, and the information from the previous discourse D has been represented in FOL CCT can be expressed: L U W U M U D |= ¬Φ

To put it another way… All-Our-Background-Stuff |= ¬Φ hence |= All-Our-Background-Stuff → ¬Φ (Deduction Theorem) Consequence: we can reduce CCT to deciding the validity of a single formula.

Informativity Checking Task (ICT) in FOL Let Φ be the FOL semantic representation of the latest sentence in some ongoing discourse Suppose that the relevant lexical knowledge L, world knowledge W, natural language metaphysical assumption M, and the information from the previous discourse D has been represented in FOL ICT can be expressed: L U W U M U D |= Φ

To put it another way… All-Our-Background-Stuff |= Φ hence |= All-Our-Background-Stu → Φ (Deduction Theorem) Consequence: we can also reduce ICT to deciding the validity of a single formula.

Yes but … This definition is semantic, i.e. given in terms of models. This is very abstract, and defined in terms of all models. There are a lot of models, and most of them are very large. So is it of any computational interest whosoever?

Proof Theory Proof theory is the syntactic approach to logic. It attempts to define collections of rules and/or axioms that enable us to generate new formulas from old That is, it attempts to pin down the notion of inference syntactically. P |- Q versus P |= Q

Examples of Proof Systems Natural deduction Hilbert-style system (often called axiomatic systems) Sequent calculus Tableaux systems Resolution Some systems (notably tableau and resolution) are particularly suitable for computational purposes.

Connecting Proof Theory to Model Theory Nothing we have said so far makes any connection between the proof theoretic and the model theoretic ideas previously introduced. We must insist on working with proof systems with two special properties Soundness Completeness.

Soundness Proof Theoretic Q is provable in proof theoretic system |- Q. Model Theoretic Q is valid in model theoretic system |= Q A PT system is sound iff |- Q implies |= Q Every theorem is valid

Remark on Soundness Soundness is typically an easy property to prove. Proofs typically have some kind of inductive structure. One shows that if the first part of proof is true in a model then the rules only let us generate formulas that are also true in a model. Proof follows by induction

Completeness Proof Theoretic Q is provable in proof theoretic system |- Q. Model Theoretic Q is valid in model theoretic system |= Q A PT system is sound iff |= Q implies |- Q Every valid formula is also a theorem

Remark on Completeness Completeness is a much deeper property that soundness,and is a lot more difficult to prove. It is typically proved by contraposition. We show that if some formula P is not provable then is not valid. This is done by building a model for ¬P The 1st completeness proof for a 1st-order proof system was given by Kurt Godel in his 1930 PhD thesis.

Sound and Complete Systems So if a proof system is both sound and complete (which is what we want) we have that: |=Φ if and only if |-Φ That is, syntactic provability and semantic validity coincide. Sound and complete proof system, really capture the our semantic reality. Working with such systems is not just playing with symbols.

Blackburn’s Proposal Deciding validity (in 1st-order logic) is undecidable, i.e. no algorithm exists for solving 1st-order validity. Implementing our proof methods for 1st-order logic (that is, writing a theorem prover only gives us a semi- decision procedure. If a formulas is valid, the prover will be able to prove it, but if is not valid, the prover may never halt! Proposal –Implement theorem provers, –but also implement a partial converse tool: model builders.

Computational Tools Theorem prover: A tool that, when given a 1st- order formula Φ attempts to prove it. If Φ is in fact provable a (sound and complete) 1 st -order prover can (in principle) prove it. Model builder: a tool that, when given a 1st- order formula Φ, attempts to build a model for it. It cannot (even in principle) always succeed in this task, but it can be very useful.

Theorem Provers and Model Checkers Theorem provers: a mature technology which provides a negative check on consistency and informativity Theorem provers can tell us when something is not consistent, or not informative. Model builders: a newer technology which provides a (partial) positive check on consistency and informativity That is, model builders can tell us when something is consistent or informative.

A Possible System Let B be all our background knowledge, and Φ the representation of the latest sentence: Partial positive test for consistency: give MB B & Φ Partial positive test for informativity: give MB B & ¬Φ Negative test for consistency: give TP B → Φ Negative test for informativity: give TP B → ¬ Φ And do this in parallel using the best available software!

Clever Use of Reasoning Tools (CURT) Baby Curt No inference capabilities Rugrat Curt: negative consistency checks (naive prover) Clever Curt: negative consistency checks (sophisticated prover) Sensitive Curt: negative and positive informativity checks Scrupulous Curt: eliminating superfluuous readings Knowledgeable Curt: adding background knowledge Helpful Curt: question answering

Baby Curt computes semantic representations Curt: 'Want to tell me something?' > every boxer loves a woman Curt: 'OK.' > readings 1 forall A (boxer(A) > exists B (woman(B) & love(A, B))) 2 exists A (woman(A) & forall B (boxer(B) > love(B, A)))

Baby Curt accumulates information > mia walks Curt: 'OK.' > vincent dances Curt: 'OK.' > readings 1 (walk(mia) & dance(vincent))

But Baby Curt is stupid > mia walks Curt: 'OK.' > mia does not walk Curt: 'OK.' > ?- readings 1 (walk(mia) & - walk(mia))

Add Inference Component Key idea: use sophisticated theorem provers and model builders in parallel. The theorem prover provides negative check for consistency and informativity. The model builder provides positive check for consistency and informativity. The 1st to find a result, reports back, and stops the other

Example > Vincent is a man Message (consistency checking): mace found a result. Curt: OK. > ?- models 1 model([d1], [f(1, man, [d1]), f(0, vincent, d1)])

Example continued > Mia likes every man. Message (consistency checking): mace found a result. Curt: OK. > Mia does not like Vincent. Message (consistency checking): bliksem found a result. Curt: No! I do not believe that!

Example 2 > ?- every car has a radio Message (consistency checking): mace found a result. Message (consistency checking): bliksem found a result. Curt: 'OK.' > ?- readings 1 forall A (car(A) > exists B (radio(B) & have(A, B)))

Issues Is a logic-based approach to feasible? How far can it be pushed? Is 1st-order logic essential? Are there other interesting inference tasks? Is any of this relevant to current trends in computational linguistics, where shallow processing and statistical approaches rule? Are there applications?