Refining the Meaning of Sense labels in PDTB: “Concession”

1. Refining the Meaning of Sense labels in PDTB: “Concession” Livio Robaldo Department of Computer Science, University of Turin robaldo@di.unito.it Eleni Miltsakaki Institute for Research in Cognitive Science, UPenn elenimi@seas.upenn.edu Jerry R. Hobbs Information Sciences Institute, University of Southern California hobbs@isi.edu

2. The PDTB: Discourse relations • They are conveyed by lexical items connecting two textual spans. Example: • On the one hand, John loves Barolo. • Sohe went and ordered three cases. • On the other hand, he didn’t have much money. • Sothenhe had to cancel the order.

3. Discourse relations On the one hand – On the other hand John loves Barolo. he went and ordered three cases. So he didn’t have much money. So he had to cancel the order. then

4. PDTB The Penn Discourse Treebank is, to date, the largest annotation effort at the discourse level (more than 40000 annotations). • It encodes discourse relations associated with discourse connectives. • It includes implicit connectives. E.g. [John broke his arm]arg1. (implicit=so) [Now, he can’t cycle to work]arg2 • Annotation are made on the Penn Treebank, including approximately 1 million of words, taken from the Wall Street Journal. A brief look at the corpus…

5. Sense annotation in the PDTB Some discourse connective can be ambiguous between more than one meaning. E.g. since: Temporal Since [the first fraud was discovered in July 1986 at an office of the People’s Bank of China]arg2, [15 major cases have been found]arg1. Causal [It was a far safer deal for lenders]arg1since [NWA had a healthier cash flow and more collateral on hand]arg2.

6. Aim of the sense annotation The aim of the annotation of senses is to provide sense tags which will flag the intended interpretation of the connectives. Identifying senses has proved to be a challenging task. Fine grained or coarse grained distinctions? Extensive sense annotations studies have been carried out to disambiguate the meaning of verbs (see, for example, Propbank: http://verbs.colorado.edu/~mpalmer/projects/ace.html). Much less for discourse connectives…

7. Sense annotation in the PDTB • Connectives have fewer senses than verbs, so the task is somewhat easier. • How did we address level of granularity? For the annotations of senses in PDTB we have constructed • ahierarchical set of senses • CLASSES • Types • subtypes

8. How to annotate senses of connectives • For each instance of a discourse connective, you must pick a CLASS sense and optionally a Type or subtype • You need not identify a Type or subtype of the CLASS sense that you have picked when you are not confident making finer sense distinctions

9. Semantic CLASSes

10. Comparison CLASS Types

11. Concession The Type Concession applies when: • Arg2 event/state A implies an event/state C but • Arg1 event/state B states or implies ~C OR • Arg1 event/state A implies an event/state C but • Arg2 event/state B states or implies ~C • The former is tagged as ‘expectation’, the latter as ‘counter-expectation’.

12. Concession Subtypes • Expectation: Although [they represent only 2% of the population]arg2, [they control nearly one-third of discretionary income]arg1. • Counter-expectation: [the savings-and-loan outlays were pushed into fiscal 1990]arg1, nevertheless [the 1989 deficit still exceeded the $136 billion target set by the Gramm-Rudman deficit-reduction law by $16 billion]arg2.

13. Hobbs’ theory of eventualities • Wide-coverage logical framework for NL based on the notion of reification. • Reification: the action of making states and events (eventualities, from [Bach,81]) first-class individuals in the logic, so they can be referred to by constants and variables. We “reify” eventualities, from the Latin word ‘re(s)’ for ‘thing’: we take them to be things. • This is done by introducing, in every predicate or logical relation, a constant\variable referring to the eventuality described by the predicate.

14. Hobbs’ theory of eventualities • There are two kinds of twin predicates: Unprimed: standard FOL predicates, which do not show the reified eventuality. E.g. (give a b c) (or give(a, b, c)) Primed: reification of the FOL predicates above. They show the eventuality in the first argument. E.g. (give’ e a b c)

15. Existence • By asserting (give’e a b c) we do not assert that e really exists in the world. Conversely, by asserting (give a b c) we do. • Distinction between possible and actual eventualities: if I want to fly, my wanting exists and my flying does not. • Rexist: asserts that an eventuality really exists in the world. It holds that: (forall(x) (iff (p x) (exists (e)(and (p’ e x) (Rexist e))))) • If I want to fly, the following formula must be true in my world: (Rexist e)  (want’ e I e1)  (fly’ e1 I)

16. Eventualities are direct objects of thoughts • “Everything” may be reified to an eventuality, including boolean and logical operators, causal and temporal relations, and even tense and aspect. • Examples: (imply’ e e1 e2): the implication from e1 to e2 is reified into an eventuality e. e has to be though as ‘the state holding between e1and e2 such that whenever e1 really exists, e2 really exists too’. (not’ e1 e2): e1 is the eventuality of the e2’s not existing. • Eventualities are taken to be direct objects of thoughts. Hence they will be inserted as arguments of predicate like think, believe, etc.

17. Eventualities are direct objects of thoughts Examples: John believes that Jack wants to eat an ice cream. (Rexist’ e) ∧(believe’ e John e1) ∧(want’ e1Jack e2) ∧ (eat’ e2Jack Ic) ∧ (iceCream’ e3Ic) I believe that when water reaches 100° Celsius, it starts boiling (Rexist’ e) ∧(believe’ e I e1) ∧(when’ e1e2 e2) ∧ (reach’ e3w t) ∧ (water’ e5w) ∧ (measure’ e6 t v s) ∧ (Celsius’ e9s) ∧ (100 e10v) ∧ (start’ e4 e7) ∧ (boil’ e7w) ∧ (present’ e11e) ∧ (indicative e)

18. Axioms • In order to impose constraints on meaning, axioms on predicates are defined. • A lot of predicates and axioms on predicates have been defined to properly dealing with causality, time, scales, composite entities, modalities, etc. • The theory features a wide coverage of NL phenomena, by keeping the logic simple (first order logic).

19. Causality • The account of causality proposed in Hobbs distinguishes between the monotonic, precise notion of ‘causal complex’ and the non-monotonic, defeasible notion of ‘cause’. • When we flip a switch to turn on a light, we say that flipping the switch “caused” the light to turn on. But for this to happen, many other factors had to be in place. The bulb had to be intact, the switch had to be connected to the bulb, the power had to be on in the city, and so on. • “Flipping the switch” is the cause of “the light turns on”. But “Flipping the switch”, “the bulb is intact”, “the power is on”, etc. are in the causal complex.

20. Causality • Two predicates: (cause e1e2) and (causalComplex s e2). Axioms: If all eventualities in the causal complex really exists, so it does the effect (monotonicity) (forall (s e) (if (and (causalComplex s e) (forall (e1)(if (member e1 s)(Rexist e1)))) (Rexist e) )

21. Causality If the cause exists, we cannot say the effect exists too (defeasability). Nevertheless, if the effect does not exist, we may infer from the previous axiom that an element of the causal complex does not exist. Those eventualities that are not normally true are identified as causes (cf. Keyser et al., 2000). So while the notion of causal complex gives us a precise way of thinking about causality, it is not adequate for the kind of practical reasoning we do in planning, explaining, and predicting. For this, we need the defeasible notion of ‘cause’.

22. Semantics of concession Two tasks: • Provide data-driven semantic descriptions (in Hobbs’ logic) that would help draw appropriate inferences. • Checking the consistency of the results with the data in the PDTB, and studying if it possible to generalize\extend them to other tags. Examples: • Although [John studied hard]arg2 [he did not pass the exam]arg1 • Although [running is considered healthy]arg2, [it is not advisable for persons with heart problems]arg1. • Although [they represent only 2% of the population]arg2, [they control nearly one-third of discretionary income]arg1.

23. Semantics of concession Two main claims: • We claim that a concessive relation arises from a contrast between the effects of two causal relations ccand cd(which are eventualities) holding in the domain. c and d stand for ‘creates’ and ‘denies’ respectively. • In all cases of concession it seems that what really creates the expectation is a causal relation cca, more abstract of cc. The real existence of cc is inherited by cca. On the contrary, there is not an abstract counterpart cdafor cdthat also really exists in the world.

24. Semantic of concession (exist (cc cac ec1ec2cd ed1ed2sc2sd2sdp ecp edp) (cause’ cc ec1ec2) ∧(cause’ cd ed1ed2) ∧ (Rexist cac) ∧(partialInstance cc cac) ∧ (Rexist cd) ∧(cause ed1edp) ∧ (Rexist ec1) ∧ (Rexist ed2) ∧(causalComplex sd2ed2) ∧ (forall (e) (if (membere sd2) (Rexist e)))∧ (Rexist edp) ∧(causalComplex sdp edp) ∧ (forall (e) (if (membere sdp) (Rexist e)))∧ (inconsistent ec2ed2) ∧ (causalComplex sc2ec2) ∧(member ecp sc2) (inconsistent edp ecp) )

25. Semantics of concession “Although John studied hard, he did not pass the exam” • Two causal relations in contrast of one another: • “John studied hard” causes “John pass the exam” The causal relation hold in that the more abstract causal relation “Studying hard causes passing exams” is assumed to hold. • Nevertheless, John did not pass the exam. There is a particular (usually unknown) reason for this, e.g. “John was very tired during the exam”. Therefore, “John was very tired” caused “John did not pass the exam” Note that “John was very tired” causes an eventuality inconsistent with the causal complex of “John pass the exam”.

26. Semantics of concession (exist (cc cac ec1ec2cd ed1ed2sc2sd2sdp ecp edp) (cause’ cc ec1ec2) ∧(cause’ cd ed1ed2) ∧ (Rexist cac) ∧(partialInstance cc cac) ∧ (Rexist cd) ∧(cause ed1edp) ∧ (Rexist ec1) ∧ (Rexist ed2) ∧(causalComplex sd2ed2) ∧ (forall (e) (if (membere sd2) (Rexist e)))∧ (Rexist edp) ∧(causalComplex sdp edp) ∧ (forall (e) (if (membere sdp) (Rexist e)))∧ (inconsistent ec2ed2) ∧ (causalComplex sc2ec2) ∧(member ecp sc2) (inconsistent edp ecp) ) ec1: John studied hard ec2: John passed the exam cca: Studying hard causes passing exams. …and that is inconsistent with another eventuality ecp in the causal complex of ec2.. ecp may be “John does not have any particular health problem that jeopardizes the passing of his exam”. ec2 is clearly inconsistent with ed2 …this is the contrast that triggers the concessive relation between Arg1 and Arg2. ed1: John was very tired ed2: John did not pass the exam cd: John was very tired caused John did not pass the exam. “studying hard causes passing exams” is assumed to exist. cc inherits real existence. However, both causal relations are defeasible! “John was very tired” is the cause of another eventuality edpthat really exists and …. “John studied hard” and “John did not pass” the exam really exist. They are available in Arg1 and Arg2. ed1 and ec2have to be inferred. Note that the causal relation is strictly continget! We cannot say that “being tired causes not passing exams”.

27. Semantics of concession “Although running is considered healthy, it is not advisable for persons with heart problems.” • Two causal relations in contrast of one another: • It is assumed that “what is considered healthy for humans is advisable for them”. This partially instantiates in “Since running is considered healthy for persons with heart problems, it is advisable for them. • Nevertheless, running is not advisable for persons with hearth problems. There is a particular reason for this, e.g. “the heart of such persons does not tolerate heartbeat increase”.

28. Semantics of concession (exist (cc cac ec1ec2cd ed1ed2sc2sd2sdp ecp edp) (cause’ cc ec1ec2) ∧(cause’ cd ed1ed2) ∧ (Rexist cac) ∧(partialInstance cc cac) ∧ (Rexist cd) ∧(cause ed1edp) ∧ (Rexist ec1) ∧ (Rexist ed2) ∧(causalComplex sd2ed2) ∧ (forall (e) (if (membere sd2) (Rexist e)))∧ (Rexist edp) ∧(causalComplex sdp edp) ∧ (forall (e) (if (membere sdp) (Rexist e)))∧ (inconsistent ec2ed2) ∧ (causalComplex sc2ec2) ∧(member ecp sc2) (inconsistent edp ecp) ) ec1: Running is considered healthy ec2: Running is advisable for people with hearth problems. cca: what is considered healthy is advisable for humans. ec2 is clearly inconsistent with ed2 …this is the contrast that triggers the concessive relation between Arg1 and Arg2. ed1: people with heart problems do not tolerate heartbeat increase ed2: Running is not advisable for people with heart problems. cd: The fact that people with heart problems do not tolerate heartbeat increase causes running to be not advisable for them. …and that is inconsistent with another eventuality ecp in the causal complex of ec2.. ecp may be “heart can tolerate heartbeat increase”. “what is healthy for humans is advisable for them” is assumed to exist. cc inherits real existence. However, both causal relations are defeasible! “Heart does not tolerate heartbeat increase” is the cause of another eventuality edp that really exists and …. “Running is considered healthy” and “Running is not advisable for people with heart problems” really exist. They are available in Arg1 and Arg2. ed1 and ec2have to be inferred.

29. Semantics of concession • “Although they represent only 2% of the population, they control nearly one-third of discretionary income.” • Two causal relations in contrast of one another: • It is assumed that “representing a low percentage of the population causes controlling low percentage of income”. This partially instantiates in “Since they control 2% of population, they control low percentage of income”. • Nevertheless, they control one-third of income, which is not considered low. There is a particular reason for this, e.g. “they are very rich” or “they have a carismatic leader”, etc.

30. Semantics of concession (exist (cc cac ec1ec2cd ed1ed2sc2sd2sdp ecp edp) (cause’ cc ec1ec2) ∧(cause’ cd ed1ed2) ∧ (Rexist cac) ∧(partialInstance cc cac) ∧ (Rexist cd) ∧(cause ed1edp) ∧ (Rexist ec1) ∧ (Rexist ed2) ∧(causalComplex sd2ed2) ∧ (forall (e) (if (membere sd2) (Rexist e)))∧ (Rexist edp) ∧(causalComplex sdp edp) ∧ (forall (e) (if (membere sdp) (Rexist e)))∧ (inconsistent ec2ed2) ∧ (causalComplex sc2ec2) ∧(member ecp sc2) (inconsistent edp ecp) ) ec1: They represent 2% of population ec2: They control a low percentage of income. cca: representing low percentage of population causes controlling low percentage of income. ec2 is clearly inconsistent with ed2 …this is the contrast that triggers the concessive relation between Arg1 and Arg2. ed1: they are very rich. ed2: they control one-third of income. cd: The fact that they are very rich causes the fact that they control one-third of income. …and that is inconsistent with another eventuality ecp in the causal complex of ec2.. ecp may be “not being rich or carismatic or controlling the army”. “representing low percentage of population causes controlling low percentage of income” is assumed to exist. cc inherits real existence. However, both causal relations are defeasible! “They are very rich” is the cause of another eventuality edp that really exists and …. “They represent 2% of population ” and “They control one-third of income” really exist. They are available in Arg1 and Arg2. ed1 and ec2have to be inferred.

31. Future work The semantic of concession defined so far covers most cases of concession…but not all: • Implication: Although [working for U.S. intelligence]arg2, [Mr. Noriega was hardly helping the U.S. exclusively]arg1. • Correlation: [John will do the report]arg1 but [he'll finish it at home]arg2 • Speech Act/Pragmatic: [they threatened]arg1 but [no one did anything]arg2 Although [John ate a lot of cakes]arg2 [he did not eat all]arg1.

32. Thank you!