1 / 36

Information Status

This article discusses the importance of the given/new distinction in information status and its uses in natural language processing. It explores different models for identifying given/new information and provides examples from the Boston Directions Corpus.

mirandaw
Download Presentation

Information Status

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. Information Status

  2. Varieties of Information Status • ContrastJohn wanted a poodle but Becky preferred a corgi. • Topic/commentThe corgi they boughtturned out to have fleas. • Theme/rhemeThe corgi they boughtturned out to have fleas. • Focus/presuppositionIt was Beckywho took him to the vet. • Given/newSome wildcats bite, but this wildcat turned out to be a sweetheart.

  3. Today: Given/New • Why do we care about Given/New? • Defining Given/New: why is this hard? • Hearer-based and Discourse-based models • Uses of Given/New information in NLP • Identifying Given/New information automatically • Rule-based • Corpus-based • The Boston Directions Corpus • Laboratory studies suggest new directions

  4. Why do we care about the given/new distinction? • Building a model of the discourse • What do S and H believe to be true? • What is in their consciousness now? • What is ‘grounded’? • Speech technologies • TTS: Given information is often deaccented while new information is usually accented • ASR?

  5. Defining Given/New • Halliday ‘67: • Given: Recoverable from some form of context • New: Not recoverable • Chafe ’74 ’76: • Given: what S believes is in H’s consciousness • New: what S believes is not… • “Chafe-givenness” Yesterday I had my class disrupted by a bulldog/dog. I’m beginning to dislike dogs/bulldogs. • But not vice versa….

  6. Prince ’81: A Given/New Taxonomy • Text as set of instructions from S to H on how to construct a discourse model • Model includes discourse entities, attributes, and links between entities • Discourse entities: individuals, classes, exemplars, substances, concepts (NPs) • Entities as ‘hooks’ on which to hang attributes (Webber ’78) • Entities when first introduced are new

  7. Brand-new (H must create a new entity) I saw a dinosaur today. • Unused (H already knows of this entity) I saw your mother today. • Evoked entities are old -- already in the discourse • Textually evoked The dinosaur was scaley and gray. • Situationally evoked The light was red when you went through it. • Inferrables • Containing

  8. I bought a carton of eggs. One of them was broken. • Non-containing A bus pulled up beside me. The driver was a monkey.

  9. Given/New and Definiteness/Indefiniteness • Definiteness: subject NPs tend to be syntactically definite and old • Indefiniteness: object NPs tend to be indefinite and new I saw a black cat yesterday. The cat looked hungry. • Definite articles, demonstratives, possessives, personal pronouns, proper nouns, quantifiers like all, every signal definiteness…but… There were the usual suspects at the bar. • Indefinite articles, quantifiers like some, any, one signal indefiniteness…but…. This guy came into the room

  10. What’s wrong with a simple Hearer-centric model of given/new? • Hearer-centric information status: • Given: what S believes H has in his/her consciousness • New: what S believes H does not have in his/her consciousness • But discourse entities may also be given and new wrt the current discourse • Discourse-old: already evoked in the discourse • Discourse-new: not evoked

  11. (1) A: I’ve decided to make an appointment with Lee Bollinger. (2) B: Why do you want to see Bollinger? • Hearer status of discourse entities in 1? 2? • If B is your roommate? your mother? a guy on the subway? • Discourse status of discourse entities in 1? 2? • What would be the hearer/discourse status of discourse entities in this version? (1) A: I’ve decided to make an appointment with Lee Bollinger. (2a) B: Why do you want to see the president? (2b) B: Have you talked to his secretary?

  12. What does this new Hearer/Discourse given/new distinction provide? • A way to separate what is explicit in the discourse model from what is believed to be in speaker/hearer cognitive model • A way to explain given/new in more complex terms • To identify coreference relations • To explain deaccenting in ASR and TTS

  13. Gross Oversimplification: Given Items Tend to be Deaccented • Accenting and deaccenting: making items intonationally prominent or not • Critical to get this distinction ‘right’ in TTS • Accenting everything makes it hard for people to understand anything, e.g. I like my cat and my cat adores me. One potato, two potato, three potato,… If a discourse entity is given for one speaker then it may or may not be given for another speaker.

  14. How can we determine automatically whether a discourse entity is given or new? • A rule-based approach: • Stem the content words in the discourse • Select a window within which incoming items with the same stem as a previous entity and within this window will be labeled ‘given’ • Other items are ‘new’ • Is this hearer-based? Discourse-based? • How well does it work? • 65-75% accurate (precision) depending on genre, domain

  15. Boston Directions Corpus (Hirschberg & Nakatani ’96) • Experimental Design • 12 speakers: 4 used • Spontaneous and read versions of 9 direction-giving tasks • Corpus: 50m read; 67m spon • Labeling • Prosodic: ToBI intonational labeling • Discourse: Grosz & Sidner • Given/new (Prince ’92), grammatical function, p.o.s.,…

  16. Boston Directions Corpus: Describe how to get to MIT from Harvard d1: dsp1: step 1: enter and get tokenfirstenter the Harvard Square T stopand buy a token d2: dsp2: inbound on red linethenproceed to get on theinboundumRed Lineuh subway

  17. dp3 dsp3: take subway from hs, to cs to ksandtake the subwayfrom Harvard Squareto Central Squareand then to Kendall Square dp4: dsp4: get off T.then get off the T

  18. Hearer and Discourse Given/New Labeling first enter <HG/DN the Harvard Square T stop> and buy <HI/DN a token> then proceed to get on <HI/DN the inbound um Red Line uh subway> and take <HG/DG the subway> from <HG/DG Harvard Square> to <HG/DN Central Square> and then to <HG/DN Kendall Square> then get off <HG/DG the T>

  19. What could we do with this labeled data? • Can we predict given/new? • Can we predict what will be accented and what will be deaccented?

  20. Does Given/New Status Predict Deaccenting?

  21. What else might be at work? • Given/new and grammatical function • Hypothesis: how discourse entities are evoked in a discourse influences how ‘given’ they are • E.g., How might grammatical function and surface position interact with the accentuation of ‘given’ items? • Cases: • X has not been mentioned in the prior context • X has been mentioned, with the same grammatical function/surface position • X has been mentioned but with a different grammatical function/surface position

  22. Experimental Design • Major problem: • How to elicit ‘spontaneous’ productions while varying desired phenomena systematically? • Key: simple variations and actions can capitalize upon natural tendency to associate grammatical functions with particular thematic roles for a given set of verbs

  23. Triangle Rectangle Cylinder Diamond Octagon

  24. Context 1 Rectangle Triangle Cylinder Diamond Octagon

  25. Context 2 Triangle Rectangle Cylinder Diamond Octagon

  26. Context 3 Triangle Rectangle Cylinder Octagon Diamond

  27. Target(A) Triangle Rectangle Cylinder Diamond Octagon

  28. Target(B) Triangle Rectangle Cylinder Diamond Octagon

  29. Experimental Conditions • 10 native speakers of standard American English • Subject and experimenter in soundproof booth • Subject told to describe scenes to confederate outside the booth, visible but with providing no feedback • 10 practice scenarios • ~20 minutes per subject

  30. Prosodic Analysis • Target turns excised and analyzed by two judges independently for location of pitch accents for each referring expression: accented (2), unsure (1), deaccented (0)  accentedness score from 0-4 (81% agreement for 0 and 2 scores)

  31. Grammatical Role/Surface Position Accenting

  32. Findings • In general • Items that differ from context to target in grammatical function or surface position tend to be accented • Items that share grammatical function and surface position tend to be deaccented • But • Subjects tend to be accented more often than objects, even if previously mentioned in the same role • Direct objects and pp-objects tend to be more distinguished from subjects than from one another

  33. How can we explain these observations? • Consider our examples, e.g. subjD.O. The TRIANGLE touches the CYLINDER. The triangle touches the DIAMOND. The triangle touches the OCTAGON. The RECTANGLE touches the TRIANGLE. • An entity may be ‘given’ or ‘new’ wrt the role it plays in the discourse

  34. Given/New Sensitive to the Role the Discourse Entity Plays • E.g., a discourse entity may retain a given or take on a new thematic role • By the time the target is uttered, ‘triangle’ is established both as a ‘given’ discourse entity and as the discourse topic (or BLC in centering theory) • But this status has been established for ‘triangle’ as agent • What is new, and, perhaps, focused in the target is ‘triangle’s’ new thematic role as patient – the players are the same but the roles are different

  35. Consequences for NLP • Identification of given/new status must be sensitive to more complex model of context (grammatical function/thematic role) • Will this help us predict deaccenting more accurately? • Stay tuned…..

  36. Next Class

More Related