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Coherence and Coreference

Coherence and Coreference. Introduction to Discourse and Dialogue CS 359 October 2, 2001. Publicly Available Telephone Demos. Nuance http://www.nuance.com/demo/index.html Banking: 1-650-847-7438 Travel Planning: 1-650-847-7427 Stock Quotes: 1-650-847-7423

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Coherence and Coreference

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  1. Coherence and Coreference Introduction to Discourse and Dialogue CS 359 October 2, 2001

  2. Publicly Available Telephone Demos • Nuance http://www.nuance.com/demo/index.html • Banking: 1-650-847-7438 • Travel Planning: 1-650-847-7427 • Stock Quotes: 1-650-847-7423 • SpeechWorks http://www.speechworks.com/demos/demos.htm • Banking: 1-888-729-3366 • Stock Trading: 1-800-786-2571 • MIT Spoken Language Systems Laboratory http://www.sls.lcs.mit.edu/sls/whatwedo/applications.html • Travel Plans (Pegasus): 1-877-648-8255 • Weather (Jupiter): 1-888-573-8255 • IBM http://www.software.ibm.com/speech/overview/business/demo.html • Mutual Funds, Name Dialing: 1-877-VIA-VOICE From Caroenter and Chu-Carroll, Tutorial on Spoken Dialogue Systems, ACL ‘99

  3. Discussion questions • What to say/how to say it distinction: Part of determining “how to say it” necessarily depends on “reading” the hearer accurately. To what extent could a computer system gauge the myriad factors - expression, body language, gesture, past utterances - to “read” the hearer? Is it a question of understanding, programming or processing?

  4. Discussion questions • How is a set of texts chosen? What makes a text good for this type of analysis? Why recipes? • How could a system cope with anaphora when there is insufficient information to resolve it at the time of utterance? • How well do systems really do at resolving extended chains of reference? • How would these systems deal with the more complex hierarchical, embedded discourse structures that we see in the real world?

  5. Agenda • Coherence: Holding discourse together • Coherence types and relations • Reference resolution • Syntactic & semantic constraints • Syntactic preferences • A first resolution algorithm

  6. Coherence: Holding Discourse Together • Cohesion: • Necessary to make discourse a semantic unit • All utterances linked to some preceding utterance • Expresses continuity • Key: Enables hearers to interpret missing elements, through textual and environmental context links

  7. Cohesive Ties (Halliday & Hasan, 1972) • “Reference”: e.g. “he”,”she”,”it”,”that” • Relate utterances by referring to same entities • “Substitution”/”Ellipsis”:e.g. Jack fell. Jill did too. • Relate utterances by repeated partial structure w/contrast • “Lexical Cohesion”: e.g. fell, fall, fall…,trip.. • Relate utterances by repeated/related words • “Conjunction”: e.g. and, or, then • Relate continuous text by logical, semantic, interpersonal relations. Interpretation of 2nd utterance depands on first

  8. Reference Resolution • Match referring expressions to referents • Syntactic & semantic constraints • Syntactic & semantic preferences • A 1st resolution algorithm

  9. Reference (terminology) • Referring expression: (refexp) • Linguistic form that picks out entity in some model • That entity is the “referent” • When introduces entity, “evokes” it • Set up later reference, “antecedent” • 2 refexps with same referent “co-refer” • Anaphor: • Abbreviated linguistic form interpreted in context • Refers to previously introduced item (“accesses”)

  10. Referring Expressions • Indefinite noun phrases (NPs): e.g. “a cat” • Introduces new item to discourse context • Definite NPs: e.g. “the cat” • Refers to item identifiable by hearer in context • By verbal, pointing, or environment availability • Pronouns: e.g. “he”,”she”, “it” • Refers to item, must be “salient” • Demonstratives: e.g. “this”, “that” • Refers to item, sense of distance (literal/figurative) • One-anaphora: “one” • One of a set, possibly generic

  11. Syntactic Constraints • Agreement: • Number: Singular/Plural • Person: 1st: I,we; 2nd: you; 3rd: he, she, it, they • Case: we/us; he/him; they/them… • Gender: he vs she vs it

  12. Syntactic & Semantic Constraints • Binding constraints: • Reflexive (x-self): corefers with subject of clause • Pronoun/Def. NP: can’t corefer with subject of clause • “Selectional restrictions”: • “animate”: The cows eat grass. • “human”: The author wrote the book. • More general: drive: John drives a car….

  13. Syntactic & Semantic Preferences • Recency: Closer entities are more salient • Grammatical role: Saliency hierarchy of roles • e.g. Subj > Object > I. Obj. > Oblique > AdvP • Repeated reference: Pronouns more salient • Parallelism: Prefer entity in same role • Verb roles: “implicit causality”, thematic role match,...

  14. Reference Resolution Approaches • Common features • “Discourse Model” • Referents evoked in discourse, available for reference • Structure indicating relative salience • Syntactic & Semantic Constraints • Syntactic & Semantic Preferences • Differences: • Which constraints/preferences? How combine? Rank?

  15. A Resolution Algorithm • Discourse model update: • Evoked entities: • Equivalence classes: Coreferent referring expressions • Salience value update: • Weighted sum of salience values: • Based on syntactic preferences • Pronoun resolution: • Exclude referents that violate syntactic constraints • Select referent with highest salience value

  16. Salience Factors (Lappin & Leass 1994) • Weights empirically derived from corpus • Recency: 100 • Subject: 80 • Existential: 70 • Object: 50 • Indirect Object/Oblique: 40 • Non-adverb PP: 50 • Head noun: 80 • Parallelism: 35, Cataphora: -175 • Divide by 50% for each sentence distance

  17. Example • John saw a beautiful Acura Integra in the dealership. • He showed it to Bob. • He bought it.

  18. Example • John saw a beautiful Acura Integra in the dealership. Referent Phrases Value John {John} 310 Integra {a beautiful Acura Integra} 280 dealership {the dealership} 230

  19. Example • He showed it to Bob. Referent Phrases Value John {John, he1} 465 Integra {a beautiful Acura Integra} 140 dealership {the dealership} 115 Referent Phrases Value John {John, he1} 465 Integra {a beautiful Acura Integra, it1} 420 dealership {the dealership} 115

  20. Example • He showed it to Bob. Referent Phrases Value John {John, he1} 465 Integra {a beautiful Acura Integra, it1} 420 Bob {Bob} 270 dealership {the dealership} 115

  21. Example Referent Phrases Value John {John, he1} 232.5 Integra {a beautiful Acura Integra, it1} 210 Bob {Bob} 135 dealership {the dealership} 57.5 • He bought it. Referent Phrases Value John {John, he1, he2} 542.5 Integra {a beautiful Acura Integra, it1, it2} 520 Bob {Bob} 135 dealership {the dealership} 57.5

  22. Coherence & Coreference • Cohesion: Establishes semantic unity of discourse • Necessary condition • Different types of cohesive forms and relations • Enables interpretation of referring expressions • Reference resolution • Syntactic/Semantic Constraints/Preferences • Discourse, Task/Domain, World knowledge • Structure and semantic constraints

  23. Challenges • Alternative approaches to reference resolution • Different constraints, rankings, combination • Different types of referent • Speech acts, propositions, actions, events • “Inferrables” - e.g. car -> door, hood, trunk,.. • Discontinuous sets • Generics • Time

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