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Interpretation as Abduction

2. Summary. Abduction in NLPThe TACITUS ProjectThe Abductive Commonsense Inference Text Understanding SystemWeighted AbductionSome Local Pragmatics. 3. What is abduction?. DeductionA, A ? BBInductionA(a1), A(a2),..., B(a1), B(a2), B(a3),..." x . A(x)

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Interpretation as Abduction

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    1. 1 Interpretation as Abduction Maurizio Atzori atzori@di.unipi.it

    2. 2 Summary Abduction in NLP The TACITUS Project The Abductive Commonsense Inference Text Understanding System Weighted Abduction Some Local Pragmatics

    3. 3 What is abduction? Deduction A, A ? B B Induction A(a1), A(a2),..., B(a1), B(a2), B(a3),... " x . A(x) Ţ B(x) Abduction is inference to the best explanation

    4. 4 Logic as the Language of Thought The six keys of Cognitive processes Conjunction of concepts (P ? Q) Modus Ponens Recognition of Obvious Contradictions Predicate-Argument Relations We can relate different concept together Universal Instantiation In other words: First-order logic! With no double negations or contrapositives

    5. 5 Nonmonotonic Logic as the Reasoning of Thought Monotonic logic: KB¦ A Ţ KBČX ¦ A Nonmonotonic: KB¦ A Ţ KBČX ¦ A E.g.: negation as failure KB bird(x) Ů Řabnormal_bird(x)Ţ fly(x) pinguin(x) Ţ abnormal_bird(x) bird(a) fly(a) ? true KB = KB Č {pinguin(a)} fly(a) ? false

    6. 6 Discourse Understanding People understand discourse because they know so much How is knowledge used in the interpretation of discourse? We need to build a KB of commonsense and domain knowledge Local pragmatics Reference resolution Interpretation of compound nominals Syntactic/lexical ambiguity Metonymy resolution

    7. 7 Sentence Interpretation Prove the logical form of the sentence Together with the constraints that predicates impose on their arguments Allowing for coercions Merging redundancies where possible Making assumptions where necessary

    8. 8 Concrete Example A cargo train running from Lima to Lorohia was derailed before dawn today after hitting a dynamite charge. Inspector Eulogio Flores died in the explosion. The police reported that the incident took place past midnight in the Carahuaichi-Jaurin area. Incident: Location Peru: Carahuaichi-Jaurin (area) Incident: Type Bombing Physical Target: Description “cargo train” Physical Target: Effect Some damage: “cargo train” Human Target: Name “Eulogio Flores”

    9. 9 Concrete Example: Inferences Hitting a dynamite charge = booming The target = train that hit the charge The human target = in the explosion Incident = hitting of the dynamite charge In order to get the location

    10. 10 TACITUS Syntactic analysis / Semantic translation component (DIALOGIC) Obtained mergin a large grammar of English with a semantic translator for all the rules (DIAGRAM Project, Linguistic String Project) Produce a logical form of the sentence (no KB) Pragmatic component Produces an elaborated logical form: inferences, assumptions, coreferences are explicited (KB) Task component Outputs the desired answer (e.g. diagnosis or database entries)

    11. 11 Most- or least-specific abduction? In many AI application, “most-specific abduction” is used E.g.: In NLP application: Sometimes “least-specific abduction” is better E.g. “fluid”: we don’t want to abduce “lube oil” Sometimes “most-specific” is better E.g. “alarm sounded. Flow obstructed” and “the alarm is for the lube oil pressure”: we want to abduce that the flow is of “lube oil”

    12. 12 Weighted Abduction: desiderata A new abduction scheme (3 features) Goals should be assumable Assumption at various levels of specificity Redundacy of text should be taken into account (yielding more economic proofs)

    13. 13 Weighted Abduction: solution Every conjunct in the logical form of the sentence is given an assumability cost If cost(Q)=c then cost(P1) is w1c If ($...,x,y,...) ...,q(x)20,q(y)10,... Then ($...,x,...) ...,q(x)10,... leading to minimality through redundancies Eg.

    14. 14 Weighted Abduction: examples How much does it cost to prove Q? C, or 0.6 if we already know P1 or P2 Q1? Least-specific: $10 Q1 Ů Q2? Most-specific! $18 instead of $20!

    15. 15 Weighted Abduction: “et cetera” (" x) lube-oil(x) Ţ fluid(x) It is abductively unuseful “Flow obstructed. Metal particles in lube oil filter” ($ x) lube-oil(x) but we cannot infer fluid(x) ? (" x) fluid(x) Ţ lube-oil(x) It “works” but we haven’t such an axiom It is false! (" x) fluid(x) Ů etc1(x) Ţ lube-oil(x) etc(x) is something like “abnormal” (special) fluid It can only be assumed, never proved

    16. 16 Local Pragmatics Phenomena Definite Reference I bought a new car last week. The car is already giving me trouble. I bought a new car last week. The vehicle is already giving me trouble. I bought a new car last week. The engine is already giving me trouble. The engine of my new car is already giving me trouble. KB (" x) car(x) Ţ vehicle(x) (" x) car(x) Ţ ($ x) vehicle(x)

    17. 17 Lexical Ambiguity John wanted a loan. He went to the bank. KB bank1(x) Ţ bank(x) “banca” bank2(x) Ţ bank(x) “riva” loan(y) Ţ financial-institution(x) Ů issue(x,y) financial-institution(x) Ů etc1(x) Ţ bank1(x) river(z) Ţ bank2(x) Ů borders(x,z)

    18. 18 Lexical Ambiguity: Abduction ... Ů bank(x) Ů ... bank1(x) Ţ bank(x) financial-institution(x) Ů etc1(x) Ţ bank1(x) loan(y) Ţ financial-institution(x) Ů issue(x,y) loan(L)

    19. 19 Compound Nominals Turpentine jar. ($ x, y) turpentine(y) Ů jar(x) Ů nn(y, x) KB (" y) liquid(y) Ů etc1(y) Ţ turpentine(y) (" e1, x, y) function(e1, x) Ů contain’(e1, x, y) Ů liquid(y) Ů etc2 (e1, x, y) Ţ jar(x) If the function of something is to contain liquid, then it may be a jar (" e1, x, y) contain’(e1, x, y) Ţ nn(y, x)

    20. 20 Compound Nominals: Abduction turpentine(y) Ů nn(y, x) Ů jar(x) liquid(y) Ů etc1(y) Ţ turpentine(y) contain’(e1, x, y) Ţ nn(y, x) liquid(y) Ů function(e1, x) Ů contain’(e1, x, y) Ů etc2 (e1, x, y) Ţ jar(x)

    21. 21 Other Local Pragmatics Exploiting Redundancy Coreference Problems Distinguishing the Given and the New

    22. 22 Integration with other approaches Interpretation as abduction Parsing as deduction It becomes possible to integrate syntax, semantics and pragmatics in a very thorough and elegant way.

    23. 23 Applications Text understanding TACITUS Project at SRI Equipment failure reports Naval operations reports Terrorist reports Question Answering! FALCON’s postprocessor makes use of this abductive framework Select the right answer among some candidate documents

    24. 24 References (1/3) Hobbs, Jerry R., 2001. Abduction in Natural Language Understanding, to appear in L. Horn and G. Ward (eds.), Handbook of Pragmatics, Blackwell Thomason, Richmond H., and Jerry R. Hobbs, 1997. Interrelating Interpretation and Generation in an Abductive Framework, Proceedings, AAAI Fall Symposium Workshop on Communicative Action in Humans and Machines, Cambridge, Massachusetts, November 1997, pp. 97-105 Hobbs, Jerry R., 1992. Metaphor and Abduction, in A. Ortony, J. Slack, and O. Stock, eds., Communication from an Artificial Intelligence Perspective: Theoretical and Applied Issues, Springer-Verlag, Berlin, pp. 35-58. Also published as SRI Technical Note 508, SRI International, Menlo Park, California. August 1991

    25. 25 References (2/3) Hobbs, Jerry R., Douglas E. Appelt, John Bear, Mabry Tyson, and David Magerman, 1991. The TACITUS System: The MUC-3 Experience, SRI Technical Note 511, SRI International, Menlo Park, California. November 1991 Stickel, M.E., 1991. A Prolog-like inference system for computing minimum-cost abductive explanations in natural-language interpretation. Annals of Mathematics and Artificial Intelligence 4 (1991), 89-106 Hobbs, Jerry R., and Megumi Kameyama, 1990. Translation by Abduction, in H. Karlgren, ed., Proceedings, Thirteenth International Conference on Computational Linguistics, Helsinki, Finland, Vol. 3, pp. 155-161, August, 1990

    26. 26 References (3/3) Tyson, Mabry, and Jerry R. Hobbs, 1990. Domain-Independent Task Specification in the TACITUS Natural Language System, Technical Note 488, Artificial Intelligence Center, SRI International, May 1990 Hobbs, Jerry R., 1990. An Integrated Abductive Framework for Discourse Interpretation, Proceedings, AAAI Spring Symposium on Abduction, Stanford, California, March 1990 Hobbs, Jerry R., 1989. The Use of Abduction in Natural Language Processing, Proceedings, Nagoya International Symposium on Knowledge Information and Intelligent Communication, Nagoya, Japan, November 1989 Hobbs, Jerry R., and Paul Martin 1987. Local Pragmatics. Proceedings, International Joint Conference on Artificial Intelligence, pp. 520-523. Milano, Italy, August 1987.

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