1 / 23

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

ellery
Download Presentation

Coherence and Coreference

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related