1. Dialogue and Information Retrieval Dialogs on Dialogs March all the way through April 2003

2. Intersections between Dialog Systems and IR Current work Call Routing Question Answering Why so little? What else? Let’s brainstorm!

3. Call Routing Task: given a NL expression of a problem, classify (route) it in one of several categories Examples AT&T: How May I Help You British Telecom Jennifer Chu-Carroll

4. Call Routing (2) It’s a classification problem! Salience (co-ocurrence) based approaches (AT&T) IR-like approaches (J. Chu-Carroll) Treat user requests as “documents” Use VSM and cosine similarity to classify

5. The IR in Call Routing Regard the problem as text classification Do standard IR work: LSA LDA Centroid vs. KNN approaches Results? Classification perf?

6. The Dialog in Call Routing Disambiguation Easy to do based on the VSM IR approach Follow-up dialog HMIHY: frame-based follow-up dialogs Q: Is Call Routing dialog management? Q: Or is it more like understanding? Q: Why typical understanding/DM approaches fail in HMIHY-type domains?

7. Question Answering Task: answer to a question in Natural Language from a database of documents in Natural Language. Examples: http://www.ai.mit.edu/projects/infolab/ http://www.ask.com

8. IR in Question Answering Everywhere: Document indexing Retrieval … What is different from traditional IR? Some parsing/understanding of questions and documents Some language generation (?)

9. Dialog in QA Refining the question: Clarification dialogue Decide which question to ask Only for very restricted domains  uses fixed frames (Rutgers: HITIQA)

10. Why so little? Different issues: IR = lots of unstructured data, no NLP Dialog = structured data, lots of NLP Main problems: Structural mismatch NLU mismatch

11. But HUGE potential! Voice-only random access to large amounts of information (“Voice IR”): technical manuals of “in-the-field” devices (e.g. NASA) tutorial systems phone-based Google (e.g. legal information…) GUI+ for IR Learn dialog stuff from data (LM, NLG, parsing…)