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From Question-Answering to Information-Seeking Dialogs

From Question-Answering to Information-Seeking Dialogs. Jerry R. Hobbs Artificial Intelligence Center SRI International Menlo Park, California. (with Douglas Appelt, Chris Culy, David Israel, David Martin, Martin Reddy, Mark Stickel, and Richard Waldinger). Key Ideas.

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From Question-Answering to Information-Seeking Dialogs

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  1. From Question-Answeringto Information-Seeking Dialogs Jerry R. Hobbs Artificial Intelligence Center SRI International Menlo Park, California (with Douglas Appelt, Chris Culy, David Israel, David Martin, Martin Reddy, Mark Stickel, and Richard Waldinger)

  2. Key Ideas 1. Logical analysis/decomposition of questions into component questions, using a reasoning engine 2. Bottoming out in variety of web resources and information extraction engine 3. Use of component questions to drive subsequent dialogue, for elaboration, revision, and clarification 4. Use of analysis of questions to determine, formulate, and present answers. Principal Investigator: Jerry R. Hobbs, SRI International

  3. Plan of Attack Inference-Based System: Inference for Question-Answering -- this year Inference for Dialog Structure -- next year, but starting design this year Document retrieval and information extraction for question-answering: Incorporate as resource in inference-based system -- this year Principal Investigator: Jerry R. Hobbs, SRI International

  4. Composition of Informationfrom Multiple Sources GEMINI SNARK How far is it from Mascat to Kandahar? Question Decomposition via Logical Rules What is the distance between the two lat/longs? What is the lat/long of Mascat? What is the lat/long of Kandahar? Resources Attached to Reasoning Process Alexandrian Digital Library Gazetteer Alexandrian Digital Library Gazetteer Geographical Formula or www.nau.edu/~cvm/latlongdist.html Principal Investigator: Jerry R. Hobbs, SRI International

  5. Composition of Informationfrom Multiple Sources Show me the region 100 km north of the capital of Afghanistan. Question Decomposition via Logical Rules What is the capital of Afghanistan? What is the lat/long 100 km north? Show that lat/long What is the lat/long of Kabul? Terravision CIA Fact Book Alexandrian Digital Library Gazetteer Geographical Formula Resources Attached to Reasoning Process Principal Investigator: Jerry R. Hobbs, SRI International

  6. Combining Time, Space,and Personal Information Could Mohammed Atta have met with an Iraqi official between 1998 and 2001? Logical Form Question Decomposition via Logical Rules meet(a,b,t) & 1998  t 2001 at(a,x1,t) & at(b,x2,t) & near(x1,x2) & official(b,Iraq) go(a,x1,t) go(b,x2,t) Temporal Reasoning IE Engine IE Engine Geographical Reasoning Resource Attached to Reasoning Process Principal Investigator: Jerry R. Hobbs, SRI International

  7. Two Central Systems GEMINI: Large unification grammar of English Under development for more than a decade Fast parser Generates logical forms Used in ATIS and CommandTalk SNARK: Large, efficient theorem prover Under development for more than a decade Built-in temporal and spatial reasoners Procedural attachment, incl for web resources Extracts answers from proofs Strategic controls for speed-up Principal Investigator: Jerry R. Hobbs, SRI International

  8. Linguistic Variation How far is Mascat from Kandahar? How far is it from Mascat to Kandahar? How far is it from Kandahar to Mascat? How far is it betweeen Mascat and Kandahar? What is the distance from Mascat to Kandahar? What is the distance between Mascat and Kandahar? GEMINI parses and produces logical forms for most TREC-type queries Use TACITUS and FASTUS lexicons to augment GEMINI lexicon Unknown word guessing based on "morphology" and immediate context Principal Investigator: Jerry R. Hobbs, SRI International

  9. "Snarkification" Problem: GEMINI produces logical forms not completely aligned with what SNARK theories need Current solution: Write simplification code to map from one to the other Long-term solution: Logical forms that are aligned better Principal Investigator: Jerry R. Hobbs, SRI International

  10. Relating Lexical Predicatesto Core Theory Predicates "... distance ..." "how far ..." distance-between Need to write these axioms for every domain we deal with Have illustrative examples Principal Investigator: Jerry R. Hobbs, SRI International

  11. Decomposition of Questions lat-long(l1,x) & lat-long(l2,y) & lat-long-distance(d,l1,l2) --> distance-between(d,x,y) Need axioms relating core theory predicates and predicates from available resources Have illustrative examples Principal Investigator: Jerry R. Hobbs, SRI International

  12. Procedural Attachment Declaration for certain predicates: There is a procedure for proving it Which arguments are required before called lat-long(l1,x) lat-long-distance(d,l1,l2) When predicate with those arguments bound is generated in proof, procedure is exectuted. Principal Investigator: Jerry R. Hobbs, SRI International

  13. Open Agent Architecture OAA Agent SNARK GEMINI snarkify Resources via OAA Agents Principal Investigator: Jerry R. Hobbs, SRI International

  14. Use of SMART + TextPro Question Question Decomposition via Logical Rules Subquestion-1 Subquestion-3 One Resource Among Many Subquestion-2 Resources Attached to Reasoning Process SMART + TextPro Other Resources Principal Investigator: Jerry R. Hobbs, SRI International

  15. Information ExtractionEngine as a Resource SMART: Document retrieval for pre-processing TextPro: Top of the line information extraction engine Analyze NL query w GEMINI and SNARK Run TextPro over documents retrieved by SMART Retrieve best-match passage Use TextPro annotations or GEMINI analysis to extract answer from passage Principal Investigator: Jerry R. Hobbs, SRI International

  16. Linking SNARK with TextPro Type of questioned constituent Synonyms and hypernyms of word associated with p or c Call to SMART+TextPro TextSearch(EntType(?x), Terms(p), Terms(c), WSeq) & Analyze(WSeq, p(?x,c)) --> p(?x,c) Answer: Ordered sequence of strings of words Match pieces of answer strings with pieces of query Subquery generated by SNARK during analysis of query Principal Investigator: Jerry R. Hobbs, SRI International

  17. Information ExtractionEngine as a Resource SMART: Document retrieval for pre-processing TextPro: Top of the line information extraction engine Analyze NL query w GEMINI and SNARK Run TextPro over documents retrieved by SMART TextPro returns relevant templates Agent turns templates into logic for SNARK to use in proof Principal Investigator: Jerry R. Hobbs, SRI International

  18. Domain-Specific Patterns Decide upon domain (e.g., nonproliferation) Compile list of principal properties and relations of interest Implement these patterns in TextPro Implement link between TextPro and SNARK, converting between templates and logic Principal Investigator: Jerry R. Hobbs, SRI International

  19. Temporal Reasoning: Structure Topology of Time: start, end, before, between Measures of Duration: for an hour, ... Clock and Calendar: 3:45pm, Wednesday, June 12 Temporal Aggregates: every other Wednesday Deictic Time: last year, ... Principal Investigator: Jerry R. Hobbs, SRI International

  20. Temporal Reasoning: Goals Develop temporal ontology (DAML) Reason about time in SNARK (AQUAINT, DAML) Link with Temporal Annotation Standards (AQUAINT) Answer questions with temporal component (AQUAINT) Nearly complete In progress Principal Investigator: Jerry R. Hobbs, SRI International

  21. Spatial and GeographicalReasoning: Structure Topology of Space: Is Albania a part of Europe? Dimensionality Measures: How large is North Korea? Orientation and Shape: What direction is Monterey from SF? Latitude and Longitude: Alexandrian Digital Library Gazetteer Political Divisions: CIA World Fact Book, ... Principal Investigator: Jerry R. Hobbs, SRI International

  22. Spatial and GeographicalReasoning: Goals Develop spatial and geographical ontology (DAML) Reason about space and geography in SNARK (AQUAINT, DAML) Attach spatial and geographical resources (AQUAINT) Answer questions with spatial component (AQUAINT) Some capability now Principal Investigator: Jerry R. Hobbs, SRI International

  23. Dialog Modeling Key Idea: System matches user's utterance with one of several active tasks. Understanding dialog is one active task. Rules of form: property(situation) --> active(Task1) including utter(u,w) --> active(DialogTask) want(u,Task1) --> active(Task1) Understanding is matching utterance (conjunction of predications) with an active task or the condition of an inactive task. Principal Investigator: Jerry R. Hobbs, SRI International

  24. Dialog Task Model Action determined by utterance and task yes understand(a,e,t): hear(a,w) & parse(w,e) & match(e,t) no -- x unmatched Ask about x Principal Investigator: Jerry R. Hobbs, SRI International

  25. Fixed-Domain QA Evaluation Pick a domain, e.g., nonproliferation Pick a set of resources, including a corpus of texts, structured databases, web services Have expert make up 200+ realistic questions, answerable with resources + inference Divide questions into training and test sets Give sites one month+ to work on training set Test on test set Principal Investigator: Jerry R. Hobbs, SRI International

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