D. Moore, Johanna

Di Eugenio, Barbara

Rose, Carolyn Penstein

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     Edinburgh Research Explorer                                      Generating 'Distributed' Referring Expressions: an Initial ReportCitation for published version:Di Eugenio, B, D. Moore, J & Rose, CP 1996, Generating 'Distributed' Referring Expressions: an InitialReport. in Eighth International Natural Language Generation Workshop (Posters and Demonstrations) . pp.33-36.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Peer reviewed versionPublished In:Eighth International Natural Language Generation Workshop (Posters and Demonstrations)General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019Generating 'Distributed' Referring Expressions: an Initial Report Barbara Di Eugenio* Computat iona l  Linguistics Department  of Phi losophy Carnegie Mellon University Pittsburgh, PA, 15213, USA d ieugen i0andrew,  cmu. edu Johanna D. Moore Department of Computer  Science, and Learning Research and Development Center University of P i t tsburgh Pittsburgh, PA, 15260, USA jmooreOcs,  p i t t .  edu Carolyn Penstein Ros~ Computat ional  Linguistics Department of Phi losophy Carnegie Mellon University Pittsburgh, PA, 15213, USA Carolyn_.Penstein_Rose0cs.  cmu. edu 1 In t roduct ion  Consider some possible ways in which S (the Speaker) may ask H (the Hearer) to bring her an ironing board that is located in the basement: (la) Bring me the ironing board in the base- ment. (1t)) Bring me the ironing board ffom the base- ment. (lc) Co down to the basement and bring me the iwning board. Arguably, only in (la) is the location £ of the ironing board (the basement) included in the NP whose head is ironing board. 1 In (lb), £ is a modifier to the verb, from the basement. In (lc), £ is just an expectation, derived from knowledge about performing a bring action, and from the relation between the two actions - -  go • Di Eugenio and Penstein Ros6 were partially sup- ported by Carnegie Mellon Faculty Development Fund # 1-13709. LIn the appropriate context, (la) could also be inter- preted as asking H to bring the ironing board into the basement, where S is (or will be, at the time H executes the action). We currently neglect this possibility. down to the basement is a substep in the plan that achieves bring S the ironing board. In these examples, the referring expression i  the NP is not sufficient o uniquely identify the intended referent, but it is its linguistic ontext that adds other necessary constraints. This is the reason why we call these referring expres- sions distributed. To our knowledge, while many researchers have worked on generating refer- ring expressions, e.g. [Appelt, 1985], [Kronfeld, 1990], [Dale, 1992], [Pattabhiraman and Cer- cone, 1990], 2 distributed referring expressions have not been addressed yet. Note that the whole linguistic context must be taken into account while generating (lc): this is shown by the redundant and infelicitous (2) Go down to the basement and bring me the ironing board in the basement. Besides planning knowledge about the action that a certain verb denotes; lexical knowledge about that verb also comes into play. Consider 2Space [infitations don't allow us to discuss other re- searchers' approaches. 33 the contrast between (lc), (3a) aald (3b): (3a) Go down to the basement and take the ironing board uJith you (3b) Go down to the basement and put the iron- ing board away Arguably the same abstract plan - -  that we'll refer to as Fetch - -  may achieve bring x to y, take x to y and put x (away) into y: namely, go to where x is, get hold of it, and take it to y. However, these verbs differ from a lexical semantics point of view, and different expecta- tions are generated in the three cases. In (lc) H expects the ironing board to be in the basement: the expectation is strong enough that even if a second ironing board is either in sight or salient ill the discourse when S utters (lc), H won't ground a the referent of tile NP the ironing board to the one in sight (although e may presumably ask "Isn't that tile one you need?"). In (3a), the NP the ironing board must refer in the current context, by being either visible or salient in the discourse. (3b) is yet a different (:ase: H will either ground the NP the ironing board to the salient one, if there is one, otherwise he will de- lay such grounding until he is in tile basement. ill this paper, we describe our first steps to- ward generating such referring expressions. Tile implementation and experimentation are cur- rently underway. 2 Towards  a so lu t ion  Our solution is based on the integration of a principled discourse planner, LONGBOW [Young and Moore, 1994; Young et al., 1994], with a formalism that represents both lexical semantic knowledge about verbs and planning knowledge about tim actions that the verbs de- note [Di Eugenio, 1993: Di Eugenio and Web- ber, 1996]. LONGBOW is an extension to partial-order causal link (POCL) planners, in which a plan 3Note the difference between referring and grounding: an NP refers in the discourse, but it is grounded to an entity in the world. is represented as a set of partially-ordered steps connected by caalsal inks. LONGBOW extends POCL planners by introducing action decompo- sition. The representation of a plan operator is divided into an action operator, that captures preconditions and effects, and a possibly empty set of decomposition operators: each decompo- sition operator epresents a single layer expan- sion of a composite step, that provides a partial specification for tile subplan that achieves the parent step's effects given its preconditions. Representation Formalism. Di Eugenio's system is able to interpret examples such as (lc): it infers the relation between the actions in tile two conjuncts 4 and the expectations under which tile relation holds. Di Eugenio's system is crucially based on a formalism composed of two KBs. Tile first, the action taxonomy, stores lexical semantic knowledge about verbs - -  the semantic primitives are derived from Concep- tual Structures [.lackendoff, 1990]; the second, tim plan library, contains recipes [Pollack, 1986], i.e. common sense planning knowledge about the actions that tile verbs in the action taxon- omy refer to. Both are implemented in CLAS- SiC [Brachman et al., 1991], a description logic based system: the terms defined in the action taxonomy are used in tile recipes in the plan library. Among the defined recipes is Fetch. Advantages of the two systems. First, LONGBOW keeps clear track of depen- dencies among steps by means of causal links. if stepl establishes an effect which is a precon- dition P2,i for step2, a causal link is created between step1 and step2. If a third step stepa has a precondition p.%j which is identical to P~,i, and if no intervening step has undone p2,i, 5 then another causal link will be established between stepl and stepa, thus keeping (:lear track of de- pendencies among steps. This (:larity can be ex- ploited e.g., to awfid generating the redundant 4Di Eugenio's algorithm actually works on purpose clauses, rather than on p~posive conjunctions. '~LONGBOW, being based on UCPOP, is able to re- solve such threats when they arise. 34 (2), as discussed below. Second, LONGBOW allows to distinguish be- tween intended and. side effects: an effect is in- tended if it plays a causal role in a plan [Young and Moore, 1994]. This is very important from a discourse planning point of view: first, the in- tentions of the speaker have been widely recog- nized ms flmdaanental in both discourse interpre- tation and production [Grosz and Sidner, 1986: Moore aald Pollack, 1993]. Second, if H reveals that an intended effect of previous discourse was not achieved, S should try to achieve that effect again; this is not the case for side effects. Third, Di Eugenio's KR system allows ex- pressing subtle lexical distinctions ms the ones underlying bring and take. It also maintains a well specified connection between lexical def- initions and the pertinent planning knowledge. Finally, there is a natural mapping of Di Eu- genio's recipe representation to LONGBOW's. which facilitates their integration. 3 The  generat ion  process  Examples such as (lc), (an) and (3b) can be generated by assuming that the first conjunct is a step in a plan to achieve the second conjunct. 6 Here, we focus on (lc) and on intensional de- scriptions of parameters: the expectation i (h:) arises because in the Fetch recipe, the initial lo- cation of the object to be moved, the basement in our examples, is defined only intensionally. The parameter depend in INFORM-REF in Fig. 1 - -  one of the discourse operators we are experimenting with - -  is used when a pa- rameter is described intensionally. If no such dependency exists, a simpler INFORM-REF operator is invoked. In tile Action operator of INFORaM-REF, the actual description for param is computed by the flmction unique-desc. The description will be a conjunction of descrip- tors d/; if a di consists of a unary predicate ap- plied to param (SIMPLE(d/)), no recursive (:all to INFORM-REF is necessary; however, if di is 6We haven't addressed yet wh~l the system decides to expand the domain plan in this way. COMPLEX, i.e. describes a relation REL be- tween param and other objects, INFORM-REF must be recursively called to provide a descrip- tion for OTHER-PARAM (di). 7 INFORaM-REF establishes two effects: that the agent is able to identify param, and also able to identify depend. Thus, when the plan- ner generates the first conjunct ct = Go down to the basement, in the context of the plan for [3 = bring S the ironing board. INFORM- REFa, s is invoked to generate the description the basement: it establishes that the agent is able to identify tile basement, and also that the agent recognizes tile dependency between the basement and the object to be moved. When planning to generate f3, INFORM-REF,& is called to generate the description the iron- ing board in the basement, and thus, INFOR_M- REFz2 is recursively called to establish the ef- fects that the agent identifies the basement, and the locational dependency between basement and ironing board. However, such effects have already been established, so this step won't be expanded; rather, the appropriate causal rink will be generated, 9 thus "explaining" why the expectation that the ironing board is in the basement comes about. Thus, no redundant de- scription of tile kind illustrated in (2) will be generated. Note that out method doesn't rest on 0/being generated before [3: if the order were reversed, INFORM-REFo2 would be expanded, and INFORM-REFa wouldn't. However, the appropriate causal links would still be estab- lished. 4 Conc lus ions  We see our work ms synthesizing several themes: tile generation of referring expressions; the spe- t in this case, we must avoid infinite regress, that may arise when using relations in deriving referring expres- sions, as discussed by [Dale and Haddock, 1991]. 8The subscript is meant to distinguish different in- stantiations of INFORM-lZEF in the plan. 'aThe new causal ink will link INFORM-REF~ and whatever other step would have been using INFORM- R.EFz~'s effects as preconditions. 35 Action Header: INFORM-REF(S, H, param, depend) Preconditions: ~ ABLE(H, idendfy(param)) Effects: ABLE(H, identify(param)) A ABLE (H, identify (depend)) Decomposition Header: Constraints: Steps: INFORM-REF(S, H, param, depend) 3 unique-desc(H, param, depend, dlA...Ad~) Start, '4 d,, SIMPLE(d~): INFORM(S, H, d,), '¢ d~, COMPLEX(d~): INFORM(S, H, REL(d,)), INFORM-REF(S, H, OTHER-PARAM(d~), d~), Final ,Figure 1: The INFORM-REF  operator  cific demands  of instructional text: and issues of Knowledge Representat ion,  among which the need to represent both  lexical and planning knowledge about  action verbs. We still have quite some work to do. The inte- grat ion of the two systems is current ly raider- way, and so is the imp lementat ion  and refine- ment  of the discourse operators  (some of the domain operators,  are those a lready defined in Di Eugenio's  KR  system).  We also have to ;~dd bring and take to the a~:tion taxonomy. References [Appelt, 1985] Douglas Appelt. Planning English Sen- tences. Studies in Natural Language Processing. Cam- bridge University Press, 1985. [Brachman et al., 1991] Ronald Brachman, Deborah McGuinness, Peter Patel-Schneider, Lori Alperin Resnick, and Alexander Borgida. Living with CLAS- SIC: When and How to Use a KL-ONE-like Language. In .John F. Sowa, editor, Principles of Semantic Net- works -- Explorations in the Representation f Knowl- edge, pages 401-456. Morgan Kaufmann, 1991. [Dale and Haddock, 1991] Robert Dale and Nichol~Ls Haddock. Content determination i the generation of referring expressions. Computational Intelligence, 7(4):252--265, 1991. [Dale, 1992] Robert Dale. Generating Referrin 9Expres- sions. ACL-MIT Series in Natural Language Process- ing. The MIT Press, 1992. [Di Eugenio and Webber, 1996] Barbara Di Eugenio and Bonnie Lynn Webber. Pragmatic Overloading in Natural Language Instructions. International Journal of Expert Systems, Special Issue on Knowledge Rep- resentation and Reasoning for Natural Language Pro- cessing, 9(2), March 1996. To appear. [Di Eugenio, 1993] Barbara Di Eugenio. Understanding Natural Language Instructions: a Computational Ap- proach to Purpose Clauses. PhD thesis, University of Pennsylvania, December 1993. Technical Report MS- CIS-93-91. [Grosz and Sidner, 1986] Barbara Grosz and Candace Sidner. Attention, Intentions, and the Structure of Discourse. Computational Linguistics, 12:175-204, 1986. [Jackendoff, 1990] Ray Jackendoff. Semantic Structures. Current Studies in Linguistics Series. The MIT Press, 1990. [Kronfeld, 1990] Amichai Kronfeld. Reference and Com- putation. Studies in Natural Language Processing. Cambridge University Press, 1990. [Moore and Pollack, 1993] Johanna Moore and Martha Pollack. A Problem ['or RST: the Need tbr Multi- Level Discourse Analysis. Computational Linguistics, 18(4):537-544, 1993. [Pattabhiraman d Cercone, 1990] T. Pattabhiraman and Nick Cercone. Selection: Salience, relevance and the coupling between domain-level tasks and text planning. In Proceedings of the Fifth International Workshop on Natural Language Generation, Pitts- burgh, Pennsylvania, pages 79-86, 1990. [Pollack, 1986] Martha Pollack. Inferring Domain Plans in Question-Answering. PhD thesis, University of Pennsylvania, 1986. [Young and Moore, 1994] R. Michael Young and Jo- hanna D. Moore. DPOCL: A Principled Approach to Discourse Planning. In Seventh International Work- shop on Natural Language Generation, pages 13-20, Kennebuakport, Maine, 1994. [Young et al., 1994] R. Michael Young, Martha E. Pol- lack, and Johanna D. Moore. Decomposition and causality in partial order planning. In Second Interna- tional Conference on Artificial Intelligence and Plan- ning Systems, 1994. 36 

Generating 'Distributed' Referring Expressions: an Initial Report

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Generating 'Distributed' Referring Expressions: an Initial Report

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