Semantic Interpretation of Pragmatic Clues: Connectives, Modal Verbs, and Indirect Speech Acts
Michael GERLACH, Michael SPRENGER
University of Hamburg Department of Computer Science, Project WISBER, P.O. Box 302762 Jungiusstrasse 6, I)-2000 Hamburg 36, West Germany
Abstract mann et. al. 87] and generate entries for the user model Representations of utterances in IRS still contain uninter preted linguistic features such as modal verbs, modal Much work in current research in the field of semantic - hedges, connectives, and tense information. We are pre pragmatic analysis has been concerned with the interpre- senting methods for deriving the meaning of these features tation of natural language utterances in the context of as they occur in utterances: transforming idiomatically- dialogs. In this paper, however, we will present methods used indirect speech acts, interpreting connectives in for a primary pragmatic analysis of single utterances. Our compound sentences, and resolving ambiguities in the investigations involve problems which are not currently meaning of modal verbs by using, i.a., temporal well understood, for example how to infer the speaker's restrictions. The last chapter sketches the technical means intentions by using interpretation of connectives and used by these processes, i.e., the semantic representation modal verbs. language, the way rules are encoded, and the asscrtional knowledge base containing the user model. This work k,; part of the joint project WlSBER which is Fig. 1 shows the different stages of the interpretation supported by the German Federal Ministery for Research process. First, if a connective is found, the analysis process and Technology. The partners in the project are: Nixdorf breaks up the sentence into separate propositions, in the Computer AG, SCS GmbH, Siemens AG, the University of next step idomatically-used indirect speech acts are Hamburg and the University of Saarbrticken. transformed into a direct question. The propositions are then interpreted independently during the modal verb analysis which creates one or more propositional attitudes Introduction for each proposition. These interpretations arc then related, depending on the natural language connective. Much work in current research in the field of semantic - Finally, after inferring the appropriate time intervals pragmatic analysis has been concerned with the inter- from verb tense, the sentence type is used to derive the pretation of ~Latural language utterances in the context of propositional attitudes which are entered into the user dialogs, e.g., determining the speaker's goals [Allen 83], model. deriving beliefs of one agent about another [Wilks/Bien 83], and planning speech acts [Appelt 85]. In this paper, however, we will present methods for a primary pragmatic analysis of ,~Jingle utterances to construct user model Transformation of Idiomatically-Used Indirect entries which are the starting point for the higher level Speech Acts inference processes just mentioned. Our investigations Speakers often use indirect speech acts because they want involve problems which are not currently well understood, to express politeness or uncertainty. Examples are : "Could for example, how to infer the speaker's intentions by using you please tell me which bonds have the highest interest interpretation of connectives and modal verbs. rate?", "i'd like to know which...", "I do not know which...." Our work is a part of the natural language consultation We believe that for appropriately handling such an system WISllER [Bergmann/Gerlach 87]. Consultation idiomatic use of indirect speech acts in a consultation dialogs require a much wider class of utterances to be system it is admissible to transform such utterances into a understood than other applications (e.g., for data base simplified form - the corresponding direct quest;~n. interface). In advisory dialogs wants and beliefs play a Therefore the first step in our semantic-pragmatic inter- central role. Although a consultation system must be pretation is mapping the different ways of asking capable of handling the linguistic means which are used questions onto one standard form which is the formal for expressing those attitudes, problems of how to treat representation of the equivalent direct question. modal verbs have received little attention in artificial Fig. 2 shows the ru~e which applies to the idiom "I do not intelligence and computational linguistics. know whether X." and transforms it into the represen The interpretation processes described in this paper work tation of the direct speech act "X ?" The rule formalism will with our aemantic representation !anguage IRS [Berg- be described in detail later. 191 The transformation of indirect speech acts works on the semantic level by applying rules which specify formal transformations of semantic representations of sentences. In this our approach differs from that taken in UC S~actic/Semantic Repre~ [Wilensky et. al. 84 and ZernikfDyer 85] where a phrasal lexicon is used and the semantic interpretation of idioms is done during the parsing process. [Breaking up Connectives Interpretation of Modal Verbs An adequate treatment of modal verbs is necessary for Transformation of Idioms determining the attitudes of the speaker concerning the state of affairs expressed by the proposition he is assert- ing. 1) The main problem in interpreting modal verbs is l nterpretation2f Modal Verbs i their typical ambiguity, e.g., (1) Mein Sohn sod viel Geld haben. In English the two readings are: 'My son is supposed to have a lot of money.' VS. 'I want my son to have a lot of money.' tion Our rules for disambiguating the different readings are based on information which is stored in the semantic Assertional Knowledge Base representation of the utterance: information about semantic categories of the subject of the modal verb (e.g., MUTUAL KNOWLEDGE ANIMATE, GENERIC, DEFINIT]O, the relation between the time expressed by the modal verb and the time of the pro- UserWants ] User Beliefs ] J'i ] Facts position and whether the proposition denotes a state or an event. (2) Ich habe 10000 Mark geerbt und m6chte das Geld ir~ Wertpapieren anlegem Sic sollen eine Laufzeit yon vier Jahren haben. Fig. !: The stages of the interpretation process 'I have inherited 10000 Marks and would like to invest the money in securities.' Two readings of the second sentence: Durin$" that transformation process we do not loose any "they are supposed to have a term of fbur yea,'s.' information which might be relevant to the dialog control VS. component of the system (not described in this paper). Whey should have a term of four years.' Before answering any question - direct or indirect - the system has to check whether it is able to answer that In the first reading of the second sentence the entry for the question. If this is not the ease the user must be informed user model must contain the proposition embedded in a about the limitations of the system's competence, anyway. belief context, while the second reading must lead to an This argumentation is similar to that of [Ellman 83], who entry under speaker's wants. In order to resolve this argues that it is not relevant whether an utterance is a ambiguity, the rules compare the time of the proposition request or an inform as long as the hearer can detect the with the tense of the modal verb. For example, if the tense speaker's superordinate goals. of the modal verb is present and the time of the proposition is sometime in the future, the system decides that the "want" reading is appropriate. The problem in our example is to determine the time of the proposition: We have only the information of tense haben (to have) which is a present (AND (ASSERTION?A) infinitive and might also denote a future state. Hence the (HAS-AGENT ?A USER) (HAS-PROP ?A ?P) system tries to final out whether the object of the propo- (PROP ?P sition appears in a Want context of the speaker. This is the (NOT case as is clear from the previous utterance ... and I wan$ to (AND (KNOW ?K) invest the money in securities and therefore the ~y~tem (HAS-EXPERIENCER?K USER) (HAS-OBJECT ?K ?X))))) decides to put the propesition of the ~c~nd sent~t~e into the user's want ~ontext as well. (Even if the second utterance is taken to be a belief of the ~peaker, the fact that it is cited in this context is sufficient to infer that it is (AND (QUESTION?O) also a want, why else should the speaker cite this fact in (HAS-AGENT ?O USER) connection with his decision to invest in securities?) (HAS-OBJECT ?0 ?X)) L 1) For the semantics of English modal verbs, which is quite different from the German, see [Boyd/Thorne 69]. Fig. 2: A rule for transformlng tho~idic~m: For German modal verbs see [Brttnner/Redder 83], '7 do not know X." [Rei~wvin 77], [Spr~nge r 88]. 102 ' Usually the user's questions are interpreted as user wants (8) Ich will eine Anlage mit kurzer Laufzeit, damit ich to knowp (or more formally: (WANT USER (KNOW USER P))), schnell an mein Geld herankommen kann. where ~ th;notes the propositional content of the question. For example, 'i want a short term investment so that I can get my money back quickly.' (3) K0nnen Pfandbriefe mehr als 7% Rendite haben? 'C~n bonds have an interest rate of more then 7%?' Because of the connective damit the system concludes that the proposition of the second part of the sentence is the is interpreted as: the user wants to know whether the superordinate goal rather than the first proposition al- proposition is true, which means in our example, taking though this is the want which is expressed directly. The into account the modal verbk6nnen, whether it is possible user supposes that the first proposition is a necessary for bonds to have an interest rate ofmore then 7 %. condition for the second, which expresses his goal. When One problem arises when the modal verb sollen occurs in a further processing this logical structure, the system can question. Normally it is interpreted as indicating a want, recognize the underlying misconception, namely that it is e.g., not the term of an investment which is important for getting the money back quickly, but the liquidity. (4) Soil ich das Fenster schliegen? 'l]hould I close the window?' The interpretation of connectives depends on the occur~ rence of modal verbs, as the following examples demon- Here the speaker wants to know, whether there is some strate: other pers~m (probably the hearer), who wants the propo- sition to be true. But this interpretation doesn't make any (9) Soll ich meine Wertpapiere verkaufen, urn racine sense in a consulting dialog. Ina consultation the speaker Hypothek ztt bezahlen? is not interested in the wants of the advisor, e.g., 'Should I sell my securities to pay off my mort- gage?' (5) Soll ich Pfandbriefe mit 5% Rendite kaufen? (10) Muff ich Gebtihren bezahlen, um mein Sparbuch 'l~hould I buy bonds which have an interest rate aufzulhsen? of 5 %?' 'Do I have to pay a fee to desolve my savings Rather than inquiring about someone else's wants, as in account? q (4), the speaker is interested in a recommendation: (WANT USER (KNOW USER (I~,ECOMMI,IND SYSTEM P~) In (9) the modal verb sollen inside the question indicates that the user wants a recommendation. It indicates further The interpretation of modal verbs is further infiuet~ood by that the connective um-zu has to be interpreted as a user's eonnectiw~;; which may occur in complements. Consider want. The correct interpretation is that the user wants to the following sentence: know whether the system would recommend that the user (6) Meine Sehwester mug viel Geld habcn. attempts to attain a certain goal (paying off his mortgage) 'My sister nmst have a lot of money.' by selling his securities. In this case one can only infer that the speaker bo!ieves Such a want is not inferrable from (10). It may be that the that the proposition is true, namely that his sister has a lot user wants to desotve his savings account at somc time in of money. The interpretation completely changes when we the future, but the modal verb mtissen (must) inside the have: question does not indicate a current want. Therefore only (7) Meine Schwester mug viel Geld haben, um th~ Haus the relation between the two propositions is the focus of zu bauen. attention. Hence we can paraphrase the user's want as 'Do ':My sister needs to have a lot of money in order to I have to pay a fee if I want to desolve my savings bnild her house.' account?', or, again more formally, It is possible that the speaker believes as in (6) that his (WANT USER (KNOW USER (IMPLIES P2 PLY)), sister has s lot of money, but this cannot be inferred from where P2 denotes the desolving event and P1 the fee the statement. Here we can only infer that the speaker paying. believes that the second proposition (his sister's building her house) implies the first one (his sister's having a lot of money). The Computational Model
The processes described in this paper work on a formal representation of utterances which reflects their semantic Connectivos structure but also contains lexical and syntactic informa- Connective~ are a means of expressing the argumentative tion (hedges, connectives, modal verbs, tense, and mood) and logical structure of the speaker's opinions by linking' which has not yet been interpreted. Our formal representa- propositions. Such relations between propositions are tion language is called IRS (Interne Reprtisenta- classified into several categories such as inferential, tionsSprache, [Bergmann et. al. 87]). It contains all the temporal, causal linkages [Cohen 84 and Br6e/Smit 86]. standard operators of predicate calculus, formalisms for The system interprets underlying beliefs and wants and expressing propositional attitudes, modalities, and speech enters them into the user nmdel in accordance with the acts, natural language connectives (and. or, however, different classes of connectives. therefore, etc.), a rich collection of natura/!anguage quant- As an example, take the class of connectives which express ifiers (e.g., articles, wh-particles), and modal operators inferences of the speaker, e.g., (maybe, necessarily). 193 ((EXIST AI (ASSERTION AI)) ((EXIST PI (PROP P1 ((EXIST $I (SOLLEN $I)) ((EXIST P2 (AND (PROP P2 Die Wertpapiere sollen ((DPL Wl (SECURITYWl)) ((EXISTTI (AND (DURAIIONTI) eine Laufzeit yon vier (HAS-UNIT T1 YEAR) Jahren haben. (HAS-AMOUNT T1 4))) (HAS-TERM Wl T1)))) 'The securities should/are (HAS-TENSE P2 PRESENT-INFINITIV))) (AND (HAS-PROPS1 P2) supposed to have a term of (HAS-TENSE $1 PRESENT))))))) four years.' (AND (HAS-AGENTA1 USER) (HAS-PROP AI P1))))
Fig. 3: An example of IRS and the corresponding part of the syntax of IRS
Fig. 3 shows a part of the syntax definition of IRS and the A quite simple example for an IRS pattern is given in representation of the sentence Fig. 4. Its elements are (6) Die Wertpapiere sollen eine Laufzeit yon vier variables (symbols starting with '?'), Jahren haben constants (all other symbols), 'The securities should/are supposed to have a a concept pattern (matching any one-place predication), term of four years.' role patterns (matching two-place predications). This example contains some important features of IRS: Only one- and two-place predicates are allowed. They correspond to the concepts and roles defined in our (AND (?INFO-TRANS-TYPE ?INFO-TRANS) terminological knowledge base QUIRK [Bergmann/ (HAT-SOURCE ?INFO-TRANS USER) Gerlach 86] except for SOLLEN and HAS-TENSE which (HAT-GOAL ?INFO-TRANS SYS) still need to be semantically interpreted. (HAT-OBJ ECT ?INFO-TRANS ?OBJECT)) Quantifications are always restricted to a range which may be described by an arbitrary formula. Fig. 4: An IRS pattern The operator PROP allows for associating a variable to a formula. In subsequent terms the variable may be used as a denotation of the proposition expressed by that This pattern is used for matching the top level of the formula. representation of an utterance of the user, directed to the In the formula given in Fig. 3 the variable A1 denotes the system. When matching the variable ?OBJECT is bound to assertion as an action with agent USER and propositional the whole propositional content of the utterance and is content P1. $1 reflects the occurrence of the modal verb used by the subsequent steps of analysis. sollen which is represented like a predicate, but has not yet As described above, we do not only infer new user model been semantically interpreted. The "propositional content" information directly, but also perform transformations on of S1 is P2 which denotes the proposition the securities have IRS structures, e.g., to reduce idioms to more primitive a term offouryears. speech acts. This kind o£ processing involves applying a set For characterizing sets of structures to which one specific oftransformatlonal rules to an IRS formula where a rule is interpretation may apply, we use IRS patterns[Gerlach a pair of IRS patterns as described above (for an example, 87], i.e., highly parameterized semantic structures which see Fig. 2). When instantiating the right hand side of the specify an arbitrary combination of features relevant to rule the interpreter will create new variables for unbound the interpretation process: The surface speech act, tense pattern variables and quantify them in the appropriate information, modal hedges, and restrictions on the way (in Fig. 2 this is the case with the pattern variable ?Q). propositional content. 194, In WISBER the user model is a section of the central asser- Cohen 84: tional knowledge base (A-Box, [Poesio 88]) which allows R. Cohen: A Computational Theory of the Function of for storing and retrieving assertional knowledge in differ- Clue Words in Argument Understanding, in: Pro- ent contexts which denote the content of propositional atti- ceedings of COLING-84, Stanford 1984, pp. 251 - 258 tudes of agents. Hence a new entry is added to the user Ellman 83: model by storing the propositional content in the A-Box J. Ellman: An Indirect Approach to Types of Speech context which contains the user's wants. Acts, in: Proc. of the 8th IJCAI, Karlsruhe 1983, pp. 600-602 Gerlach 87: M. Gerlach: BNF - a Tool for Processing Formally Defined S~]ntactic Structures. Universit~t Hamburg, Conclusior~ Fachberelch Informatik, WISBI~R-Memo Nr. 17, Dezember 1987 We have implemented our interpretation module in an Interlisp programming environment. It is a part of the Naito et. al. 85: natural lahguage consultation '~ystem WISBER. The S. Naito / A Shimazu / H. Nomura: Classification of module's coverage includes all German modal verbs occur- Modality Function and its Application to Japanese Language Analysis, in: Proceedings of the 23rd ing in assections and questions, some connectives (e.g., Annual Meeting of the ACL, Chicago 1985, pp. 27-34 • and, so that, because) and the most common indirect questions. On the one hand our future work will Poesio 88: concentrate on extending the performance of the system M. Poesio: Dialog-Oriented A-Boxing, Universit/~t Hamburg, Fachbereich Informatik, WISBER Bericht, inside the framework which is described in this paper. On to appear. the other hand we will integrate the concept of expecta- tions, i.e. expectations the system has according to the Reinwein 77: users next utterance depending on the actual state of the J. Reinwein: Modalverb-Syntax, T~ibingen 1977 dialog. Thi~ will enable us to resolve more kinds of ambi- Sprenger 88: guities in user utterances. M. Sprenger: Interpretation yon Modalverben zu; Konstruktion yon Partnermodelleintragen, Universitfit Hamburg, Fachbereich lnformatik, WISBER Menlo Nr. 18, Januar 1988 Wilensky et. al. 84: References R. Wilensky, Y. Arens, D. Chin: Talking to UNIX in English: an Overview of UC, in: Communications of the ACM 27(6), pp. 574-593 (June 1984) Allen 83: J. F. Allen: Recognizing Intentions from Natural Wilks/Bien 83: Language Utterances, in: M. Brady and R. C. Berwick Y. Wilks, J. 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