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CPSC 503 Computational Linguistics

CPSC 503 Computational Linguistics. Discourse and Dialog Lecture 14 Giuseppe Carenini. Finish form (22/10). Word Sense Disambiguation Word Similarity Semantic Role Labeling. Semantic Role Labeling: Example. Some roles. Employer. Employee. Task. Position.

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CPSC 503 Computational Linguistics

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  1. CPSC 503Computational Linguistics Discourse and Dialog Lecture 14 Giuseppe Carenini CPSC503 Winter 2008

  2. Finish form (22/10) • Word Sense Disambiguation • Word Similarity • Semantic Role Labeling CPSC503 Winter 2008

  3. Semantic Role Labeling: Example Some roles.. Employer Employee Task Position • In 1979 , singer Nancy Wilson HIRED himto open her nightclub act . • Castro has swallowed his doubts and HIRED Valenzuela as a cook in his small restaurant . CPSC503 Winter 2008

  4. Supervised Semantic Role Labeling Typically framed as a classification problem [Gildea, Jurfsky 2002] • Train a classifier that for each predicate: • determine for each synt. constituent which semantic role (if any) it plays with respect to the predicate • Train on a corpus annotated with relevant constituent features These include: predicate, phrase type, head word and its POS, path, voice, linear position…… and many others CPSC503 Winter 2008

  5. Semantic Role Labeling: Example ARG0 [issued, NP, Examiner, NNP, NPSVPVBD, active, before, …..] predicate, phrase type, head word and its POS, path, voice, linear position…… CPSC503 Winter 2008

  6. Supervised Semantic Role Labeling (basic) Algorithm • Assign parse tree to input • Find all predicate-bearing words (PropBank, FrameNet) • For each predicate.: apply classifier to each synt. constituent Unsupervised Semantic Role Labeling: bootstrapping [Swier, Stevenson ‘04] CPSC503 Winter 2008

  7. Knowledge-Formalisms Map(including probabilistic formalisms) Understanding Generation State Machines (and prob. versions) Logical formalisms (First-Order Logics) Morphology Syntax Rule systems (and prob. versions) Semantics Pragmatics Discourse and Dialogue AI planners (MDPs Markov Decision Processes) CPSC503 Winter 2008

  8. Today 27/10 • Brief Intro Pragmatics • Discourse • Monologue • Dialog CPSC503 Winter 2008

  9. Sentence “Semantic” Analysis Meanings of grammatical structures Syntax-driven and Lexical Semantic Analysis Meanings of words Literal Meaning I N F E R E N C E Common-Sense Domain knowledge Further Analysis Discourse Structure Intended meaning Context Pragmatics CPSC503 Winter 2008

  10. Pragmatics: Example (i) A: So can you please come over here again right now (ii) B: Well, I have to go to Edinburgh today sir (iii) A: Hmm. How about this Thursday? What information can we infer about the context in which this (short and insignificant) exchange occurred ? CPSC503 Winter 2008

  11. Pragmatics: Conversational Structure (i) A: So can you please come over here again right now (ii) B: Well, I have to go to Edinburgh today sir (iii) A: Hmm. How about this Thursday? Not the end of a conversation (nor the beginning) • Pragmatic knowledge: Strong expectations about the structure of conversations • Pairs e.g., request <-> response • Closing/Opening forms CPSC503 Winter 2008

  12. Pragmatics: Dialog Acts (i) A: So can you please come over here again right now (ii) B: Well, I have to go to Edinburgh today sir (iii) A: Hmm. How about this Thursday? • A is requesting B to come at time of speaking, • B implies he can’t (or would rather not) • A repeats the request for some other time. • Pragmatic assumptions relying on: • mutual knowledge (B knows that A knows that…) • co-operation (must be a response… triggers inference) • topical coherence (who should do what on Thur?) CPSC503 Winter 2008

  13. Pragmatics: Specific Act (Request) (i) A: So can you please come over here again right now (ii) B: Well, I have to go to Edinburgh today sir (iii) A: Hmm. How about this Thursday? • A wants B to come over • A believes it is possible for B to come over • A believes B is not already there • A believes he is not in a position to order B to… Pragmatic knowledge: speaker beliefs and intentions underlying the act of requesting Assumption: A behaving rationally and sincerely CPSC503 Winter 2008

  14. Pragmatics: Deixis (i) A: So can you please come over here again right now (ii) B: Well, I have to go to Edinburgh today sir (iii) A: Hmm. How about this Thursday? • A assumes B knows where A is • Neither A nor B are in Edinburgh • The day in which the exchange is taking place is not Thur., nor Wed. (or at least, so A believes) Pragmatic knowledge: References to space and time wrt space and time of speaking CPSC503 Winter 2008

  15. Today 27/10 • Brief Intro Pragmatics • Discourse • Monologue • Dialog CPSC503 Winter 2008

  16. Discourse: Monologue (like sentences as sequences of words) • Monologues as sequences of “sentences” havestructure • Tasks: Text Segmentation and Rhetorical (discourse) parsing and generation • Key discourse phenomenon: referring expressions (what they denote may depend on previous discourse) • Task: Coreference resolution CPSC503 Winter 2008

  17. Discourse/Text Segmentation(1) • State of the art: • linear (unable to identify hierarchical structure) • Subtopics, passages • UNSUPERVISED • Key idea: lexical cohesion (vs. coherence) • “There is not water on the moon. Andromeda is covered by the moon.” • Discourse segments tend to be lexically cohesive • Cohesion score drops on segment boundaries CPSC503 Winter 2008

  18. Discourse/Text Segmentation(2) • SUPERVISED • Binary classifier (SVM, decision tree,…) • : make yes-no boundary decision between any two sentences • features • Cohesion features (e.g., word overlap, word cosine) • Presence of (domain specific) discourse markers • News “good evening, I am.., joining us now is…” • Real estate ads: is previous word phone number? CPSC503 Winter 2008

  19. Sample Monologues: Coherence House-A is an interesting house. It has a convenient location. Even though house-A is somewhat far from the park, it is close to work and to a rapid transportation stop. It has a convenient location. It is close to work. Even though house-A is somewhat far from the park, house-A is an interesting house. It is close to a rapid transportation stop. CPSC503 Winter 2008

  20. CORE EVIDENCE Corresponding Text Structure House-A is an interesting house. CORE-1 CONCESSION-1 EVIDENCE-1 It has a convenient location. it is close to a rapid transportation stop it is close to work Even though house-A is somewhat far from the park CPSC503 Winter 2008 decomposition ordering rhetorical relations

  21. Text Relations, Parsing and Generation • Rhetorical (coherence) Relations: • different proposals (typically 20-30 rels) • Elaboration, Contrast, Purpose… • Parsing: Given a monologue, determine its rhetorical structure [Marcu, ’00 and ‘02] • Generation: Given a communicative goale.g.,[convince user to quit smoking]generate structure • Next class CPSC503 Winter 2008

  22. Reference Language contains many references to entities mentioned in previous sentences (i.e., in the discourse context/model) • I saw him • I passed the course • I’d like the red one • I disagree with what you just said • That caused the invasion • Two tasks • Anaphora/pronominal resolution • Co-reference resolution CPSC503 Winter 2008

  23. Reference Resolution • Terminology • Referring expression: NL expression used to perform reference • Referent: “entity” that is referred • Types of referring expressions: • Indefinite NP (a, some, …) • Definite NP (the, … ) • Pronouns (he, she, her,...) • Demonstratives (this, that,..) • Names • Inferrables • Generics CPSC503 Winter 2008

  24. Pronominal Resolution: Simple Algorithm • Last object mentioned (correct gender/person) • John ate an apple. He was hungry. • He refers to John (“apple” is not a “he”) • Google is unstoppable. They have increased.. • Selectional restrictions • John ate an apple in the store. • It was delicious. [stores cannot be delicious] • It was quiet. [apples cannot be quiet] • Binding Theory constraints • Mary bought herself a new Ferrari • Mary bought her a new Ferrari CPSC503 Winter 2008

  25. Additional Complications • Some pronouns don’t refer to anything • Itrained • must check if verb has a dummy subject • Evaluate “last object” mentioned using parse tree, not literal text position • I went to the GAP which is opposite to BR. • It is a big store. [GAP, not BP] CPSC503 Winter 2008

  26. Focus John is a good student He goes to all his tutorials He helped Sam with CS4001 He wants to do a project for Prof. Gray He refers to John (not Sam) CPSC503 Winter 2008

  27. Supervised Pronominal Resolution Corpus annotated with co-reference relations (all antecedents of each pronoun are marked) • What features ? (U1) John saw a nice Ferrari in the parking lot (U2) He showed it to Bob (U3) Hebought it CPSC503 Winter 2008

  28. Need World Knowledge • The police prohibited the fascists from demonstrating because they feared violence. vs • The police prohibited the fascists from demonstrating because theyadvocated violence. • Exactly the same syntax! • Not possible to resolve they without detailed representation of world knowledge about feared violence vs. advocated violence CPSC503 Winter 2008

  29. Coreference resolution • Decide whether any pair of NPs co-refer • Binary classifier again anaphor NPj antecedents • What features? • Same as for anaphora + specific ones to deal with definite and names. E.g., • Edit distance • Alias (based on type – e.g., for PERSON: Dr. or Chairman can be removed) • Appositive (“Mary, the new CEO, ….” CPSC503 Winter 2008

  30. Today 27/10 • Brief Intro Pragmatics • Discourse • Monologue • Dialog CPSC503 Winter 2008

  31. Example: ACTION-DIRECTIVE (i) A: So can you please come over here again right now (ii) B: Well, I have to go to Edinburgh today sir (iii) A: Hmm. How about this Thursday (vi) B: OK REJECT-PART ACTION- DIRECTIVE ACCEPT Discourse: Dialog • Most fundamental form of language use • First kind we learn as children Dialog can be seen as a sequence of communicative actions of different kinds (dialog acts) - (DAMSL 1997; ~20) CPSC503 Winter 2008

  32. Dialog: two key tasks • (1) Dialog act interpretation: identify the user dialog act • (2) Dialog management: (1) & decide what to say and when CPSC503 Winter 2008

  33. Dialog Act Interpretation • What dialog act a given utterance is? • Surface form is not sufficient! E.g., I’m having problems with the homework • Statement - prof. should make a note of this, perhaps make homework easier next year • Directive - prof. should help student with the homework • Information request - prof should give student the solution CPSC503 Winter 2008

  34. Automatic Interpretation of Dialog Acts State Machines (and prob. versions) Morphology Logical formalisms (First-Order Logics) Cue-based Syntax Rule systems (and prob. versions) Semantics Pragmatics Discourse and Dialogue Plan-Inferential AI planners CPSC503 Winter 2008

  35. Time-consuming: • To develop • To execute • Ties discourse processing with non-linguistic reasoning -> AI complete Plan Inferential (BDI) Pros/Cons • Dialog acts are expressed as plan operators involving belief, desire, intentions • Powerful: uses rich and sound knowledge structures -> should enable modeling of subtle indirect uses of dialog acts CPSC503 Winter 2008

  36. Cue-Based: Key Idea Words and collocations: • Please and would you -> REQUEST • are you and is it -> YES-NO-QUESTIONs Prosody: Loudness or stress yeah -> AGREEMENT vs. BACKCHANNEL • Conversational structure: • Yeah following PROPOSAL -> AGREEMENT • Yeah following INFORM -> BACKCHANNEL CPSC503 Winter 2008

  37. Split Corpus for d1 N-gram models1 …… …… Corpus for dm N-gram modelsm Cue-Based model (1) Each dialog act type (d) has its own micro-grammar which can be captured by N-gram models Annotated Corpus • Lexical: given an utterance W= w1 …wn for each dialog act (d) we can compute P(W|d) • Prosodic: given an utterance F= f1 …fn for each dialog act (d) we can compute P(F|d) CPSC503 Winter 2008

  38. d3 d1 d5 d2 di-1 • … d4 Fi , Wi di Fi , Wi Fi , Wi Cue-Based model (2) • 1 Annotated Corpus • 1 Conversational structure: Markov chain • .3 • .8 • 1 • .2 • .2 • .3 • .5 • .7 Combine all info sources: HMM N-gram models! CPSC503 Winter 2008

  39. Combine Markov Chain and N-grams into an HMM Sequences of sequences • Now ..can be computed with …… Cue-Based model Summary • For each dialog act type (e.g., REQUEST), build lexical and phonological N-grams • Start form annotated corpus (each utterance labeled with appropriate dialog act) • Build Markov chain for dialog acts (to express conversational structure) CPSC503 Winter 2008

  40. Dialog Managers in Conversational Agents • Examples: Airline travel info system, restaurant/movie guide, email access by phone • Tasks • Control flow of dialogue (turn-taking) • What to say/ask and when CPSC503 Winter 2008

  41. Dialog Managers State Machines (and prob. versions) Morphology Logical formalisms (First-Order Logics) FSA Syntax Rule systems (and prob. versions) Semantics Template-Based Pragmatics Discourse and Dialogue BDI MDP AI planners (and prob. versions) CPSC503 Winter 2008

  42. 27/10: Probably stop here CPSC503 Winter 2008

  43. FSA Dialog Manager: system initiative • xxx CPSC503 Winter 2008

  44. Template-based Dialog Manager (1) • S: How may I help you? • U: I want to go from Boston to Baltimore on the 8th. • GOAL: to allow more complex sentences that provide more than one info item at a time • Slot Optional questions • From_Airport “From what city are you leaving?” • To_Airport “Where are you going?” • Dept-Time “When do you want to leave?” • Dept-Day …………… • ………… • Interpretation: Semantic Grammars, semi-HMM, Hidden-Understanding-Models (HUM) CPSC503 Winter 2008

  45. Template-based Dialog Manager (2) • User may provide information to fill slots in different templates • More than one template: e.g., car or hotel reservation • A set of production rules fill slots depending on input and determines what questions should be asked next E.g., IF user mention car slot and “most” of air slot are filled THEN ask about remaining car slots. CPSC503 Winter 2008

  46. Markov Decision Processes [’02] • Common formalism in AI to model an agent interacting with its environment. • States / Actions / Rewards • Application to dialog: • States: slot in frame currently worked on, ASR confidence value, number of questions about slot,.. • Actions: questions types, confirmation types • Rewards: user feedback, task completion rate CPSC503 Winter 2008

  47. BDI Dialog Manager • S1: How may I help you? • U1: I want to go to Pittsburgh in April. • S2: And, what date in April do you want to travel? • U2: Uh hmm I have a mtg. there on the 12th. Sys to understand U2 needs model of preconditions, effects, decomposition of: • meeting event (precon: be “there”) • fly-to plan (decomp: book-flight, take-flight) • Take-flight plan (effect: be “there”) REQUEST ACKNOWLEDGE REQUEST INFORM CPSC503 Winter 2008

  48. BDI Dialog Manager • S1: How may I help you? • U1: I want to go to Pittsburgh in April. • S2: And, what date in April do you want to travel? • U2: Uh hmm I have a mtg. there on the 12th. REQUEST ACKNOWLEDGE Sys to generate S2 needs model preconditions of: • Book-flight action (agent knows departure date and time) REQUEST INFORM Integrated with logic-based planning system • Generating an utterance: plan generation (possibly) satisfying multiple goals • Understanding an utterance: plan recognition (recognize multiple goals) CPSC503 Winter 2008

  49. Designing Dialog Systems: User-Centered Design • Early Focus on User and Task: e.g., interview the users • Build Prototypes: Wizard-of-Oz (WOZ) studies • Evaluation Iterative Design CPSC503 Winter 2008

  50. Next Time: Natural Language Generation • Read handout on NLG • Lecture will be about an NLG system that I developed and tested CPSC503 Winter 2008

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