E R S V I T I N A U S S S I A S R N A E V I Modeling Information Structure for Computational Discourse and Dialog Processing Ivana Kruijff-Korbayov´ a korbay@coli.uni-sb.de http://www.coli.uni-sb.de/˜korbay/esslli04/ ESSLLI 2004 Advanced Course Nancy, 16-20 August 2004 I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 1 S S I A S R N A E V I Lecture 1 Outline • Information Structure Partitioning • Question test for IS • IS Realization Means • IS Semantics • Meaning Differences due to IS • IS and Discourse Dynamics • Course Outline Reading: • Course Reader: Chapter 1: Introduction • Course Reader: Section: 2.1: Two Dimensions of IS. • For further reading suggestions see course website I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 2 S S I A S R N A E V I Motivation I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 3 S S I A S R N A E V I Motivation (1) Sign in London underground: Dogs must be carried. Can be read (capitals denote intonation center): (Halliday, 1970) (2) a. Dogs must be carried. b. Dogs must be carried. There are differences in meaning: (1 ′ ) a. If you have a dog, you must carry it. b. What you must do is carry a dog. (i.e., not allowed to enter without) • The same or similar meanings can be realized in various ways. • Different languages may use different ways. I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 4 S S I A S R N A E V I Motivation (3) German: a. Hunde mussen werden. getragen Dogs must carried be b. Es mussen getragen werden. Hunde It explet must 3pl dogs nom carried part be inf . (4) Czech: n´ a. Psi se mus´ ı est. Dogs nom refl must 3pl carry inf b. Mus´ ı se n´ est pes. Must 3sg refl carry inf dog nom I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 5 S S I A S R N A E V I Motivation Czech newspaper 1990: (Hajiˇ cov´ a) ˇ (5) Dobr´ a zpr´ ava je, ˇ ze Ceˇ si udˇ elali revoluci. Good news is that Czechs made revolution. The good news is that the Czechs made a revolution. ˇ ˇ Spatn´ a zpr´ ava je, ˇ ze revoluci udˇ elali Ceˇ si. Bad news is that revolution made Czechs. The bad news is that the/a revolution was made by the Czechs. (or: . . . it was the Czechs who made the/a revolution) I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 6 S S I A S R N A E V I Motivation Dialog with an intelligent-home application: (Kruijff-Korbayov´ a et al., 2003) (6)U: What devices are there in the house? S: There is a stove in the kitchen , a radio in the kitchen and a radio in the bathroom . U: What is the status of the radios? S: The radio in the kitchen is on . The radio in the bathroom is off . U: Which devices are on? S: The radio in the kitchen is on. The stove in the kitchen is on. • The same (default) realization would not be appropriate in all cases. • Wrong realization maybe be disturbing or misleading. • The realization of system output needs to be controlled according to context. I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 7 S S I A S R N A E V I Information Structure Partitioning I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 8 S S I A S R N A E V I What is Information Structure? • IS comprises the utterance-internal structural and semantic properties reflecting the relation of an utterance to the discourse context , in terms of the discourse status of its contents, the actual and attributed attentional states of the discourse participants, and the participants’ prior and changing attitudes (knowledge, beliefs, intentions, expectations, etc.) (Kruijff-Korbayov´ a and Steedman, 2003) • IS is represented as a partitioning of utterance meaning w.r.t. how parts of an utterance depend on and affect the context • IS is reflected in/by the surface realization of the utterance I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 9 S S I A S R N A E V I Two Dimensions of Information Structure • A partitioning of utterance meaning into what the speaker means to address and what the speaker means to say about it: Theme the part which relates it to the purpose of the discourse and anchors the content to the context (i.e., what speaker and hearer are attending to); “ point of departure ” Rheme the part which advances the discourse, i.e., adds or modifies some information “ about the Theme ” • A partitioning of utterance meaning according to which parts contribute to distinguishing the actual content from alternatives in the discourse context: Background the non-discriminating part(s), same across alternatives Focus the discriminating part(s), different from alternatives I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 10 S S I A S R N A E V I Approaches to IS Theories of IS differ in • how they define the partitioning more precisely • which of the two dimensions they concentrate on when they consider both, how they combine them – “embedded”: Sgall, Hajiˇ cov´ a et al. (CB/NB deeper within Topic and Focus; contrastive Topic, Focus proper); Valdduv´ ı (Link/Tail in Ground); Steedman (Background-Focus within Theme and Rheme) – “orthogonal”: Halliday (Thematic Structure vs. Information Stucture); Chomsky, Jackendoff . . . (Topic-Comment vs. Presupposition-Focus) I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 11 S S I A S R N A E V I Mathesius 1929 (Russell 1905) nucleus/focus known/unknown (Strawson 1950, 1954) presupposition Firbas 1964, 1966 theme/rheme Bolinger 1965 context dependent/independent theme/rheme, accent Chomsky 1965 Sgall 1967 topic/comment Halliday 1967 topic/focus, context bound/unbound theme’/rheme’ given/new (orthogonal) Karttunen 1968 (Sacks, Schegloff Chomsky 1970/Jackendoff 1970 & Jefferson 1974) Dahl 1969 (Montague 1973) presupposition/focus topic/comment (Winograd, Woods) topic/comment (orthogonal) background/focus Kay 1975 (Halliday & Hasan 1976) given/new (Cresswell, von Stechow (Grimes 1975) Karttunen & Peters 1979 Kamp, Heim) presupposition/focus (structured meanings, Gundel, Prince Chafe, Clark, (alternative set) DRT) topic/comment Selkirk 1984 given/new’ (orthogonal) (Polanyi and Scha 1983 ) Krifka, Kratzer (Brown 1983) Rooth 1985 presupposition/narrow focus, (Mann & Thompson 1987) (Grosz & Sidner, Webber) wide focus .. (Pierrehumbert & Hirschberg, Buring 1995 Grosz, Joshi & Weinstein) topic/focus Steedman 1991 Vallduvi 1990 C/Q alternatives set theme/rheme, link/tail/focus background/focus Hajicova, Partee, & Sgall 1998 Vallduvi & Vilkuna 1998 theme/rheme, topic/focus, 0/kontrast context bound/unbound Hendriks 1999 link/tail/focus I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 12 S S I A S R N A E V I Question Test for IS • Operational test of the appropriateness of a particular IS w.r.t. given context – Question represents the context – Theme reflects the question, and Rheme is what answers the question (7) Q. What does John do? A. John writes novels . � �� � � �� � T heme Rheme (8) Q. Who writes novels ? A. writes novels . John � �� � � �� � Rheme T heme • Exchanging (7.Q) and (8.Q) yields incoherent Q-A pairs. • Do not confuse with Q-A pairs in a natural dialogue! I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S cont’d 13 S S I A S R N A E V I Question Test: Focus Projection • Phonological focus: word(s) carrying pitch accent (9) John flew from London to Paris . • Semantic focus: narrow vs. broad projection of phonological focus (10) Where did John fly (to) from London? John flew from London to Paris . (narrow) (11) What flight did John make? John flew from London to Paris . (broad 1) (12) What did John do? John flew from London to Paris . (broad 2) (13) What happened? John flew from London to Paris . (broad 3) I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
E R S V I T I N A U S 14 S S I A S R N A E V I (14) From which place did John fly to Paris? John flew from London to Paris. (narrow) (15) Who flew from London to Paris? John flew from London to Paris. (narrow) (16) What happenned to Nixon? Nixon died . (narrow) (17) Who died? Nixon died. (narrow) (18) What happenned? a. Nixon died. (broad) b. Nixon died . (broad) I.Kruijff-Korbayov´ a Modeling IS for Computational Processing: Lecture 1 ESSLLI 2004
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